Photoplethysmography based atrial fibrillation detection: a review

Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning

BACKGROUND

With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-based methods has strict requirements on the quantity of labeled data. To overcome this limitation, we explore the performance of self-supervised contrastive learning in PPG-based AF detection.

METHODS

Our method initially utilizes 1,209 h of unlabeled PPG data from the VitalDB database, conducting self-supervised pretraining using two contrastive learning frameworks, SimCLR and BYOL. Subsequently, the weights of the encoder are transferred and fine-tuned on a small amount of labeled PPG data to complete the AF detection task, including the selected MIMIC III, UMass, and DeepBeat datasets. In the realm of contrastive learning, we investigated seven data augmentation operations to explore their composite and preferred combinations, as well as the effects of double-sided and single-sided transformations.

RESULTS

Our research ultimately demonstrated that the preferred combination, incorporating single-sided transformation with the Drift operation, is most suitable for PPG data. Notably, even with only 1 %, 20 %, and 1 % of the training data from the three datasets used for fine-tuning, our approach achieves better F1 scores compared to supervised learning on the respective complete training sets. Additionally, on the 0.01 % DeepBeat training set, fine-tuning still showed a clear advantage over supervised learning.

CONCLUSION

Appropriate self-supervised contrastive pretraining effectively leverages a substantial amount of existing unlabeled PPG data, thus reducing the reliance on labeled data for AF detection, and offering a possible solution to address the limitations posed by the scarcity of labels.

Study protocol: diagnosis of atrial fibrillation in postoperative thoracic surgery using a smartwatch, an open-label randomised controlled study (THOFAWATCH trial)

INTRODUCTION

Postoperative atrial fibrillation (POAF) affects approximately 20% of patients undergoing thoracic surgery and is associated with severe complications such as stroke, myocardial infarction, heart failure, and increased mortality. Early diagnosis is critical to mitigate these risks, but conventional monitoring is limited in detecting asymptomatic episodes. Smartwatches equipped with single-lead ECG and atrial fibrillation (AF) detection algorithms offer a novel approach for early POAF detection. This study aims to evaluate the effectiveness of smartwatch-based monitoring compared with standard care in identifying POAF following thoracic surgery.

METHODS AND ANALYSIS

The THOFAWATCH trial is a randomised, bicentric open-label study enrolling 302 adult patients undergoing major thoracic surgery (pneumonectomy or lobectomy) with one-lung ventilation. Eligible patients will be randomised into two groups: (1) the 'Smartwatch Monitoring' group, where participants will undergo rhythm monitoring using a smartwatch and (2) the 'Conventional Monitoring' group, receiving standard care without smartwatch monitoring. In the intervention group, any smartwatch-detected POAF episodes will be confirmed by 12-lead ECG. The primary outcome is the incidence of POAF within 7-day postsurgery. Secondary outcomes include the rate of asymptomatic POAF, cardiovascular prognosis evaluated at 2 and 6 months (composite major adverse cardiovascular events outcome), feasibility of smartwatch usage (device usage time and success rate of single-lead ECGs) and recurrence or management of AF at follow-up. Inclusion criteria include adults (>18 years) undergoing scheduled thoracic surgery and able to use the smartwatch device. Exclusion criteria encompass patients with prior AF, those requiring telemetry, or undergoing reoperations. Statistical analysis will assess the primary outcome using χ2 or Fisher's exact test (α=5%), while secondary outcomes will include descriptive and inferential statistics, with analysis conducted using SAS V.9.4.

ETHICS AND DISSEMINATION

Ethical approval for this bicentric study has been granted by the institutional review board (IRB) of the University Hospital of Amiens (Comité de Protection des Personnes sud-ouest et outre-mer 1, 21050 Toulouse, France, registration number ID RDB: 2022-A02028-27 in November 2024). The trial is registered under ClinicalTrials.gov (ID: (NCT06724718)). Results will be disseminated through peer-reviewed publications and scientific conferences to inform clinical practice regarding POAF detection and management following thoracic surgery.

TRIAL REGISTRATION NUMBER

NCT06724718; clinical trial.

Comparison of diagnostic accuracy of electrocardiogram-based versus photoplethysmography-based smartwatches for atrial fibrillation detection: A Systematic Review and Meta-Analysis

Background

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, significantly affects morbidity and mortality, making early detection crucial for preventing stroke and heart failure. Recent advancements in wearable technology have introduced smartwatches as potential tools for continuous non-invasive AF detection.

Objective

This systematic review and meta-analysis aimed to evaluate and compare the diagnostic accuracy of electrocardiography (ECG)-and photoplethysmography (PPG)-based smartwatches in detecting AF.

Methodology

A comprehensive search was conducted on PubMed, Google Scholar, and other databases from 18 August to 23 September 2024, to fetch original studies that evaluated performance metrics of ECG and PPG smartwatches in AF detection. The obtained literature was screened according to preset inclusion and exclusion criteria. For included studies, the random-effects model was used to calculate their pooled sensitivity and specificity in AF detection using Jamovi 2.3.28 software. A significance threshold of P <0.05 was applied to all statistical analyses.

Results

Out of the 2564 studies screened, 25 met the inclusion criteria: 11 on PPG and 14 on ECG smartwatches. PPG smartwatches exhibited higher diagnostic performance with a pooled sensitivity of 97.4% (95% CI: 96.5-98.3) and specificity of 96.6% (95% CI: 94.9-98.3). Conversely, ECG smartwatches showed a pooled sensitivity of 83% (95% CI: 78-88) and specificity of 88.4% (95% CI: 84.5-92.2), lower than PPG smartwatches.

Conclusion

PPG-based smartwatches outperformed ECG-based devices in AF detection, offering higher sensitivity and specificity. Even though both modalities are effective in AF detection, the considerable variability in ECG smartwatch performance highlights the need for further research and standardization.

TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals

BACKGROUND AND PURPOSE

Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals.

METHOD

We have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion.

RESULTS

Our proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation.

CONCLUSION

Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.

Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

Fully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects

Cardiomechanical monitoring techniques record cardiac vibrations on the chest via lightweight electrodeless sensors that allow long-term patient monitoring. Heartbeat detection in cardiomechanical signals is generally achieved by leveraging a simultaneous electrocardiography (ECG) signal to provide a reliable heartbeats localization, which however strongly limits long-term monitoring. A heartbeats localization method based on template matching has demonstrated very high performance in several cardiomechanical signals, with no need for a concurrent ECG recording. However, the reproducibility of that method was limited by the need for manual selection of a heartbeat template from the cardiomechanical signal by a skilled operator. To overcome that limitation, this study presents a fully automated version of the template matching method for ECG-free heartbeat detection, powered by a novel automatic template selection algorithm. The novel method was validated on 256 Seismocardiography (SCG), Gyrocardiography (GCG), and Forcecardiography (FCG) signals, from 150 healthy and pathological subjects. Comparison with all existing methods for ECG-free heartbeat detection was carried out. The method scored sensitivity and positive predictive value (PPV) of 97.8% and 98.6% for SCG, 96.3% and 94.5% for GCG, 99.2% and 99.3% for FCG, on healthy subjects, and of 85% and 95% for both SCG and GCG on pathological subjects. Statistical analyses on inter-beat intervals reported almost unit slopes (R2 > 0.998) and limits of agreement within ± 6 ms for healthy subjects and ± 13 ms for pathological subjects. The proposed automated method surpasses all previous ECG-free approaches in heartbeat localization accuracy and was validated on the largest cohort of pathological subjects and the highest number of heartbeats. The method proposed in this study represents the current state of the art for ECG-free monitoring of cardiac activity via cardiomechanical signals, ensuring accurate, reproducible, operator-independent heartbeats localization. MATLAB® code is released as an off-the-shelf tool to support a more widespread and practical use of cardiomechanical monitoring in both clinical and non-clinical settings.

Patients perspectives on integrating eHealth in regular care pathways for atrial fibrillation: evaluating photoplethysmography for remote self-assessment

AIMS

Smartphone applications for heart rate and rhythm assessment are increasingly used for the management of atrial fibrillation (AF). Although the use of a photoplethysmography (PPG)-based smartphone application with subsequent (tele)consultations for AF management has been proven feasible in the TeleCheck-AF project, specific needs, and expectations of patients with AF are unclear. The aim of this study is to evaluate patients' perspectives on the use of remote PPG-based electronical health (eHealth) integrated in regular care pathways for AF.

METHODS AND RESULTS

A qualitative study was conducted among patients with known AF, who have used a PPG-based smartphone application around scheduled (tele)consultations. Semi-structured interviews were audio-recorded and transcribed verbatim. Data were analysed according to conventional content analysis.In total, 14 patients were interviewed. Five main themes were defined after analysis, i.e. smartphone application usability, requirements for eHealth implementation, remote self-assessment, patient engagement, and blended care (i.e. combining digital and face-to-face care). Overall, the participants were positive about the use of the PPG-based smartphone application and subsequent (tele)consultation. Using this application made the participants feel involved and led to active participation. In addition, the healthcare provider-patient relationship appeared an important aspect for adequate implementation. Particularly, timely consultation was found important, to discuss the results with their healthcare provider.

CONCLUSION

The results of this study emphasize the importance of blended care for the implementation of remote PPG-based eHealth in AF management. The use of a PPG-based smartphone application in regular care can support patient engagement and subsequently the process of shared decision making.

Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation

Atrial fibrillation (AF) is recognized as a developing global epidemic responsible for a significant burden of morbidity and mortality. To counter this public health crisis, the advancement of artificial intelligence (AI)-aided tools and methodologies for the effective detection and monitoring of AF is becoming increasingly apparent. A unified strategy from the international research community is essential to develop effective intelligent tools and technologies to support the health professionals for effective surveillance and defense against AF. This review delves into the practical implications of AI-aided tools and techniques for AF detection across different clinical settings including screening, diagnosis, and ambulatory monitoring by reviewing the revolutionary research works. The key finding is that the advance in AI and its use for automatic detection of AF has achieved remarkable success, but collaboration between AI and human intelligence is required for trustworthy diagnostic of this life-threatening cardiac condition. Moreover, designing efficient and robust intelligent algorithms for onboard AF detection using portable and implementable computing devices with limited computation power and energy supply is a crucial research problem. As modern wearable devices are equipped with sophisticated embedded sensors, such as optical sensors and accelerometers, hence photoplethysmography and ballistocardiography signals could be explored as an affordable alternative to electrocardiography (ECG) signals for AF detection, particularly for the development of low-cost and miniature screening and monitoring devices.

Transformer-based heart language model with electrocardiogram annotations

This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, an arrhythmia specified as an irregular heart rhythm without patterns. We construct a language with tokens from heartbeat locations to detect irregular heart rhythms by applying a transformers-based neural network architecture previously used only for building natural language models. Our experiments include 41, 128, 256, and 512 tokens, representing parts of ECG recordings after tokenization. The method consists of training the foundation model with annotated benchmark databases, then finetuning on a much smaller dataset and evaluating different ECG datasets from those used in the finetuning. The best-performing model achieved an F1 score of 93.33 % to detect AFIB in an ECG segment composed of 41 heartbeats by evaluating different training and testing ECG benchmark datasets. The results showed that a foundation model trained on a large data corpus could be finetuned using a much smaller annotated dataset to detect and classify arrhythmia in ECGs. This work paves the way for the transformation of foundation models into invaluable cardiologist assistants soon, opening the possibility of training foundation models with even more data to achieve even better performance scores.

Generative adversarial networks with fully connected layers to denoise PPG signals

Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.Approach.A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.Main results.The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm.Significance.The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms

OBJECTIVE

Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data. This study aims to reliably detect atrial fibrillation (AF) from short-term single-lead (STSL) electrocardiogram (ECG) recordings obtained in unsupervised telehealth environments.

METHODS

A novel deep ensemble-based method was developed for detecting AF from STSL ECG recordings. Following this, a postprocessing algorithm was created to assess uncertainty in classified STSL ECGs and to refrain from interpretation when confidence is low. The proposed method was validated through a 5-fold cross-validation on the Cardiology Challenge 2017 (CinC2017) dataset.

RESULTS

The deep ensemble method achieved 83.5 1.5% sensitivity, 98.4 0.2% specificity, and an F-score of 0.847 0.016in AF detection. Implementing the selective classification algorithm resulted in significant improvements, with sensitivity increasing to 92.8 2.2%, specificity to 99.7 0.0%, and an F-score of 0.919 0.016.

CONCLUSION

The proposed method demonstrates the feasibility of accurately detecting AF from STSL ECG recordings. The selective classification approach offers a substantial enhancement to automated ECG interpretation algorithms in telehealth solutions.

SIGNIFICANCE

These findings highlight the potential for improving the utility of telehealth systems by integrating advanced CDSSs capable of managing uncertainty and ensuring higher accuracy, thereby improving patient outcomes in remote healthcare settings.

Developing technologies to assess vascular ageing: a roadmap from VascAgeNet

Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

From Wristbands to Implants: The Transformative Role of Wearables in Heart Failure Care

BACKGROUND

Heart failure (HF) management increasingly relies on innovative solutions to enhance monitoring and care. Wearable devices, originally popularized for fitness tracking, show promise in clinical decision-making for HF. This study explores the application and potential for the broader integration of wearable technology in HF management, emphasizing remote monitoring and personalized care.

METHODS

A comprehensive literature review was performed to assess the role of wearables in HF management, focusing on functionalities like vital sign tracking, patient engagement, and clinical decision support. Clinical outcomes and barriers to adopting wearable technology in HF care were critically analyzed.

RESULTS

Wearable devices increasingly track physiological parameters relevant to HF, such as heart rate, physical activity, and sleep. They can identify at-risk patients, promote lifestyle changes, facilitate early diagnosis, and accurately detect arrhythmias that lead to decompensation. Additionally, wearables may assess fluid status, identifying early signs of decompensation to prevent hospitalization and supporting therapeutic adjustments. They also enhance physical activity and optimize cardiac rehabilitation programs, improving patient outcomes. Both wearable and implanted cardiac devices enable continuous, non-invasive monitoring through small devices. However, challenges like data integration, regulatory approval, and reimbursement impede their widespread adoption.

CONCLUSIONS

Wearable technology can transform HF management through continuous monitoring and early interventions. Collaboration among involved parties is essential to overcome integration challenges and validate most of these devices in clinical practice.

Multiclass arrhythmia classification using multimodal smartwatch photoplethysmography signals collected in real-life settings

In the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning. However, deep learning requires a large amount of data and independent testing on diverse datasets, to ensure the generalizability of the model, and most prior studies did not meet these requirements. Moreover, most prior studies using wearable-based PPG sensor data collection were limited either to controlled environments, to minimize motion artifacts, or to short duration data collection. Most importantly, frequent premature atrial and ventricular contractions (PAC/PVC) can confound most AF detection algorithms. This has not been well studied, largely due to limited datasets containing these rhythms. Note that the recent deep learning models show 97% AF detection accuracy, and the sensitivity of the current state-of-the-art technique for PAC/PVC detection is only 75% on minimally motion artifact corrupted PPG data. Our study aims to address the above limitations using a recently completed NIH-funded Pulsewatch clinical trial which collected smartwatch PPG data over two weeks from 106 subjects. For our approach, we used multi-modal data which included 1D PPG, accelerometer, and heart rate data. We used a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) deep learning model to detect three classes: normal sinus rhythm, AF, and PAC/PVC. Our proposed 1D-Bi-GRU model's performance was compared with two other deep learning models that have reported some of the highest performance metrics, in prior work. For three-arrhythmia-classification, testing data for all deep learning models consisted of using independent data and subjects from the training data, and further evaluations were performed using two independent datasets that were not part of the training dataset. Our multimodal model achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. Our model was computationally more efficient (14 times more efficient and 2.7 times faster) and outperformed the best state-of-the-art model by 20.81% for PAC/PVC sensitivity and 2.55% for AF accuracy. We also tested our models on two independent PPG datasets collected with a different smartwatch and a fingertip PPG sensor. Our three-arrhythmia-classification results show high macro-averaged area under the receiver operating characteristic curve values of 96.22%, and 94.17% for two independent datasets, demonstrating better generalizability of the proposed model.

Photoplethysmography-based non-invasive blood pressure monitoring via ensemble model and imbalanced dataset processing

Photoplethysmography, a widely embraced tool for non-invasive blood pressure (BP) monitoring, has demonstrated potential in BP prediction, especially when machine learning techniques are involved. However, predictions with a singular model often fall short in terms of accuracy. In order to counter this issue, we propose an innovative ensemble model that utilizes Light Gradient Boosting Machine (LightGBM) as the base estimator for predicting systolic and diastolic BP. This study included 115 women and 104 men, with experimental results indicating mean absolute errors of 5.63 mmHg and 9.36 mmHg for diastolic and systolic BP, in line with level B and C standards set by the British Hypertension Society. Additionally, our research confronts data imbalance in medical research which can detrimentally affect classification. Here we demonstrate an effective use for the Synthetic Minority Over-sampling Technique (SMOTE) with three nearest neighbors for handling moderate imbalanced datasets. The application of this method outperformed other methods in the field, achieving an F1 score of 81.6% and an AUC value of 0.895, emphasizing the potential value of SMOTE for addressing imbalanced datasets in medical research.

Pulse oximetry in pediatric care: Balancing advantages and limitations

BACKGROUND

Pulse oximetry has become a cornerstone technology in healthcare, providing non-invasive monitoring of oxygen saturation levels and pulse rate. Despite its widespread use, the technology has inherent limitations and challenges that must be addressed to ensure accurate and reliable patient care.

AIM

To comprehensively evaluate the advantages, limitations, and challenges of pulse oximetry in clinical practice, as well as to propose recommendations for optimizing its use.

METHODS

A systematic literature review was conducted to identify studies related to pulse oximetry and its applications in various clinical settings. Relevant articles were selected based on predefined inclusion and exclusion criteria, and data were synthesized to provide a comprehensive overview of the topic.

RESULTS

Pulse oximetry offers numerous advantages, including non-invasiveness, real-time feedback, portability, and cost-effectiveness. However, several limitations and challenges were identified, including motion artifacts, poor peripheral perfusion, ambient light interference, and patient-specific factors such as skin pigmentation and hemoglobin variants. Recommendations for optimizing pulse oximetry use include technological advancements, education and training initiatives, quality assurance protocols, and interdisciplinary collaboration.

CONCLUSION

Pulse oximetry is crucial in modern healthcare, offering invaluable insights into patients' oxygenation status. Despite its limitations, pulse oximetry remains an indispensable tool for monitoring patients in diverse clinical settings. By implementing the recommendations outlined in this review, healthcare providers can enhance the effectiveness, accessibility, and safety of pulse oximetry monitoring, ultimately improving patient outcomes and quality of care.

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.

Signal completion using generative adversarial networks for enhanced photoplethysmography measurement accuracy

Despite the growing adoption of wearable photoplethysmography (PPG) devices in personal health management, their measurement accuracy remains limited due to susceptibility to noise. This paper proposes a novel signal completion technique using generative adversarial networks that ensures both global and local consistency. Our approach innovatively addresses both short- and long-term PPG variations to restore waveforms while maintaining waveform consistency within and between pulses. We evaluated our model by removing up to 50 % of segments from segmented PPG waveforms and comparing the original and reconstructed waveforms, including systolic peak information. The results demonstrate that our method accurately reconstructs waveforms with high fidelity, producing natural and seamless transitions without discontinuities at reconstructed boundaries. Additionally, the reconstructed waveforms preserve typical PPG shapes with minimal distortion, underscoring the effectiveness and novelty of our technique.

Feasibility of using chest strap and dry electrode system for longer term cardiac arrhythmia monitoring: Results from a pilot observational study

BACKGROUND AND AIM

Cardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM).

METHODS

The observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed.

RESULTS

The study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal.

CONCLUSION

The chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks]

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.

Recent Advances in the Management of Non-rheumatic Atrial Fibrillation: A Comprehensive Review

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular atrial electrical activity, posing significant challenges to patient management and healthcare systems worldwide. Non-rheumatic AF, distinct from AF due to rheumatic heart disease, encompasses a spectrum of etiologies, including hypertension, coronary artery disease, and structural heart abnormalities. This review examines the latest advancements in managing non-rheumatic AF, encompassing diagnostic approaches, pharmacological therapies, and innovative non-pharmacological interventions. Diagnostic strategies ranging from traditional electrocardiography to advanced imaging modalities are explored alongside emerging biomarkers and wearable technologies facilitating early detection and management. Pharmacological management options, including novel anticoagulants and rhythm control agents, are evaluated in light of current guidelines and recent clinical trials. Non-pharmacological interventions, such as catheter ablation and device-based therapies, are discussed regarding their evolving techniques and outcomes. Special considerations for diverse patient populations, including elderly individuals and athletes, are addressed, emphasizing personalized approaches to optimize therapeutic outcomes. The review concludes with insights into future directions for AF management, highlighting promising avenues in gene therapy, regenerative medicine, and precision medicine approaches. By synthesizing recent research findings and clinical innovations, this review provides a comprehensive overview of the dynamic landscape of non-rheumatic AF management, offering insights for clinicians, researchers, and healthcare stakeholders.

The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.

A signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms contain valuable clinical information and play a crucial role in cardiovascular health monitoring, medical research, and managing medical conditions. The features extracted from PPG waveforms have various clinical applications ranging from blood pressure monitoring to nociception monitoring, while features from ABP waveforms can be used to calculate cardiac output and predict hypertension or hypotension. In recent years, many machine learning models have been proposed to utilize both PPG and ABP waveform features for these healthcare applications. However, the lack of standardized tools for extracting features from these waveforms could potentially affect their clinical effectiveness. In this paper, we propose an automatic signal processing tool for extracting features from ABP and PPG waveforms. Additionally, we generated a PPG feature library from a large perioperative dataset comprising 17,327 patients using the proposed tool. This PPG feature library can be used to explore the potential of these extracted features to develop machine learning models for non-invasive blood pressure estimation.

SleepBP-Net: A Time-Distributed Convolutional Network for Nocturnal Blood Pressure Estimation from Photoplethysmogram

Nocturnal blood pressure (BP) monitoring offers valuable insights into various aspects of human wellbeing, particularly cardiovascular health. Despite recent advancements in medical technology, there remains a pressing need for a non-invasive, cuffless, and less burdensome method for overnight BP measurements. A range of machine learning models have been developed to estimate daytime BP using photoplethysmography (PPG), a readily available sensor embedded in modern wearable devices. However, investigations into nocturnal BP estimation, especially concerning long-term data patterns during sleep, are still lacking. This paper investigates the estimation of nocturnal BP from overnight PPG signals collected in a clinical-grade sleep laboratory setting. To address this, we propose SleepBP-Net, a lightweight time-distributed convolutional recurrent network. This novel model leverages long-term patterns within PPG waveforms to estimate systolic and diastolic BP (SBP and DBP), considering Portapres BP measurements as a reference. Our experiments, based on leave-one-subject-out validation on 1-minute sequences of PPG, resulted in a mean absolute error (MAE) of 15.7 mmHg (SBP) and 12.1 mmHg (DBP). Model personalization improved the results to 7.8 mmHg (SBP) and 5.9 mmHg (DBP). Further enhancements were observed when extending the sequence length to 30 minutes, resulting in MAE values of 7.2 mmHg (SBP) and 5.7 mmHg (DBP). These findings underscore the significance of learning long-term temporal patterns from sleep PPG data. Additionally, we demonstrate the superiority of hybrid convolutional recurrent networks over their convolutional network counterparts. Based on our results, SleepBP-Net holds promise for unobtrusive real-world nocturnal BP estimation, particularly in scenarios where computational efficiency is crucial.

Finger photopletysmography detects early acute blood loss in compensated blood donors: a pilot study

Objective.Diagnosis of incipient acute hypovolemia is challenging as vital signs are typically normal and patients remain asymptomatic at early stages. The early identification of this entity would affect patients' outcome if physicians were able to treat it precociously. Thus, the development of a noninvasive, continuous bedside monitoring tool to detect occult hypovolemia before patients become hemodynamically unstable is clinically relevant. We hypothesize that pulse oximeter's alternant (AC) and continuous (DC) components of the infrared light are sensitive to acute and small changes in patient's volemia. We aimed to test this hypothesis in a cohort of healthy blood donors as a model of slight hypovolemia.Approach.We planned to prospectively study blood donor volunteers removing 450 ml of blood in supine position. Noninvasive arterial blood pressure, heart rate, and finger pulse oximetry were recorded. Data was analyzed before donation, after donation and during blood auto-transfusion generated by the passive leg-rising (PLR) maneuver.Main results.Sixty-six volunteers (44% women) accomplished the protocol successfully. No clinical symptoms of hypovolemia, arterial hypotension (systolic pressure < 90 mmHg), brady-tachycardia (heart rate <60 and >100 beats-per-minute) or hypoxemia (SpO2< 90%) were observed during donation. The AC signal before donation (median 0.21 and interquartile range 0.17 a.u.) increased after donation [0.26(0.19) a.u;p< 0.001]. The DC signal before donation [94.05(3.63) a.u] increased after blood extraction [94.65(3.49) a.u;p< 0.001]. When the legs' blood was auto-transfused during the PLR, the AC [0.21(0.13) a.u.;p= 0.54] and the DC [94.25(3.94) a.u.;p= 0.19] returned to pre-donation levels.Significance.The AC and DC components of finger pulse oximetry changed during blood donation in asymptomatic volunteers. The continuous monitoring of these signals could be helpful in detecting occult acute hypovolemia. New pulse oximeters should be developed combining the AC/DC signals with a functional hemodynamic monitoring of fluid responsiveness to define which patient needs fluid administration.

Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.

Wearable health monitoring: wave of the future or waste of time?

BACKGROUND

Atrial fibrillation is responsible for over 400,000 hospitalizations in the United States (US) each year. This costs the US health system over 4 billion each year. New smartwatches can constantly monitor pulse, oxygen saturation, and even heart rhythm. The FDA has provided clearance for select smartwatches to detect arrhythmias, including atrial fibrillation.

FINDINGS

These devices are not currently widely implemented as diagnostic tools. In this review, we delve into the mechanism of how smartwatches work as healthcare tools and how they capture health data. Additionally, we analyze the reliability of the data collected by smartwatches and the accuracy of their sensors in monitoring health parameters. Moreover, we explore the accessibility of smartwatches as healthcare tools and their potential to promote self-care among individuals. Finally, we assess the outcomes of using smartwatches in healthcare, including the limited studies on the clinical effects and barriers to uptake by the community.

CONCLUSION

Although smartwatches are accurate for the detection of atrial fibrillation, they still face many hurdles, including access to aging populations and trust in the medical community.

Photoplethysmography based atrial fibrillation detection: a continually growing field

Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.

Taslim Uddin Raju S, Hossain MI, Dipto SA, M. Tanvir Uddin S, Sijan A, Chowdhury MAS, Ahmad A, Mahamudul Hasan Khan M. A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objective

Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement.

Methods

In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately.

Results

The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP.

Conclusion

The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

Validation of Electrocardiogram Based Photoplethysmogram Generated Using U-Net Based Generative Adversarial Networks

Photoplethysmogram (PPG) performs an important role in alarming atrial fibrillation (AF). While the importance of PPG is emphasized, there is insufficient amount of openly available atrial fibrillation PPG data. We propose a U-net-based generative adversarial network (GAN) which synthesize PPG from paired electrocardiogram (ECG). To measure the performance of the proposed GAN, we compared the generated PPG to reference PPG in terms of morphology similarity and also examined its influence on AF detection classifier performance. First, morphology was compared using two different metrics against the reference signal: percent root mean square difference (PRD) and Pearson correlation coefficient. The mean PRD and Pearson correlation coefficient were 27% and 0.94, respectively. Heart rate variability (HRV) of the reference AF ECG and the generated PPG were compared as well. The p-value of the paired t-test was 0.248, indicating that no significant difference was observed between the two HRV values. Second, to validate the generated AF PPG dataset, four different datasets were prepared combining the generated PPG and real AF PPG. Each dataset was used to optimize a classification model while maintaining the same architecture. A test dataset was prepared to test the performance of each optimized model. Subsequently, these datasets were used to test the hypothesis whether the generated data benefits the training of an AF classifier. Comparing the performance metrics of each optimized model, the training dataset consisting of generated and real AF PPG showed a test accuracy result of 0.962, which was close to that of the dataset consisting only of real AF PPG data at 0.961. Furthermore, both models yielded the same F1 score of 0.969. Lastly, using only the generated AF PPG dataset resulted in test accuracy of 0.945, indicating that the trained model was capable of generating valuable AF PPG. Therefore, it can be concluded that the generated AF PPG can be used to augment insufficient data. To summarize, this study proposes a GAN-based method to generate atrial fibrillation PPG that can be used for training atrial fibrillation PPG classification models.

Cardiac arrhythmias classification using photoplethysmography database

Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs.

cbPPGGAN: A Generic Enhancement Framework for Unpaired Pulse Waveforms in Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbP PG) is a non-contact technique that measures cardiac-related blood volume alterations in skin surface vessels through the analysis of facial videos. While traditional approaches can estimate heart rate (HR) under different illuminations, their accuracy can be affected by motion artifacts, leading to poor waveform fidelity and hindering further analysis of heart rate variability (HRV); deep learning-based approaches reconstruct high-quality pulse waveform, yet their performance significantly degrades under illumination variations. In this work, we aim to leverage the strength of these two methods and propose a framework that possesses favorable generalization capabilities while maintaining waveform fidelity. For this purpose, we propose the cbPPGGAN, an enhancement framework for cbPPG that enables the flexible incorporation of both unpaired and paired data sources in the training process. Based on the waveforms extracted by traditional approaches, the cbPPGGAN reconstructs high-quality waveforms that enable accurate HR estimation and HRV analysis. In addition, to address the lack of paired training data problems in real-world applications, we propose a cycle consistency loss that guarantees the time-frequency consistency before/after mapping. The method enhances the waveform quality of traditional POS approaches in different illumination tests (BH-rPPG) and cross-datasets (UBFC-rPPG) with mean absolute error (MAE) values of 1.34 bpm and 1.65 bpm, and average beat-to-beat (AVBB) values of 27.46 ms and 45.28 ms, respectively. Experimental results demonstrate that the cbPPGGAN enhances cbPPG signal quality and outperforms the state-of-the-art approaches in HR estimation and HRV analysis. The proposed framework opens a new pathway toward accurate HR estimation in an unconstrained environment.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing

BACKGROUND

Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF.

OBJECTIVES

This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal.

METHODS

We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set.

RESULTS

The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed).

CONCLUSIONS

DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme

OBJECTIVE

We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network.

METHODS

To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases-the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing.

RESULTS

Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods.

CONCLUSION

These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices.

SIGNIFICANCE

PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson's correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.

Challenges and prospects of visual contactless physiological monitoring in clinical study

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.

Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery-a systematic review

Background

This systematic review aims to highlight the untapped potential of heart rate variability (HRV) and atrial fibrillation (AF) monitoring by wearable health monitoring devices as a critical diagnostic tool in cardiac surgery (CS) patients. We reviewed established predictive capabilities of HRV and AF monitoring in specific cardiosurgical scenarios and provide a perspective on additional predictive properties of wearable health monitoring devices that need to be investigated.

Methods

After screening most relevant databases, we included 33 publications in this review. Perusing these publications on HRV's prognostic value, we could identify HRV as a predictor for sudden cardiac death, mortality after acute myocardial infarction (AMI), and post operative atrial fibrillation (POAF). With regards to standard AF assessment, which typically includes extensive periods of unrecorded cardiac activity, we demonstrated that continuous monitoring via wearables recorded significant cardiac events that would otherwise have been missed.

Results

Photoplethysmography and single-lead electrocardiogram (ECG) were identified as the most useful and convenient technical assessment modalities, and their advantages and disadvantages were described in detail. As a call to further action, we observed that the scientific community has relatively extensively explored wearable AF screening, whereas HRV assessment to improve relevant clinical outcomes in CS is rarely studied; it still has great potential to be leveraged.

Conclusions

Therefore, risk assessment in CS would benefit greatly from earlier preoperative and postoperative AF detection, comprehensive and accurate assessment of cardiac health through HRV metrics, and continuous long-term monitoring. These should be achievable via commercially available wearables.

Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21 mmHg and 3.00 mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller

At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.

Multispectral sensor fusion in SmartWatch for in situ continuous monitoring of human skin hydration and body sweat loss

Post-pandemic health operations have become a near-term reality, discussions around wearables are on the rise. How do wearable health solutions effectively deploy and use this opportunity to fill the gap between wellness and healthcare? In this paper, we will talk about wearable healthcare diagnosis, with a particular focus on monitoring skin hydration using optical multi-wavelength sensor fusion. Continuous monitoring of human skin hydration is a task of paramount importance for maintaining water loss dynamics for fitness lovers as well as for skin beauty, integrity and the health of the entire body. Preserving the appropriate levels of hydration ensures consistency of weight, positively affects psychological state, and proven to result in a decrease in blood pressure as well as the levels of "bad" cholesterol while slowing down the aging processes. Traditional methods for determining the state of water content in the skin do not allow continuous and non-invasive monitoring, which is required for variety of consumer, clinical and cosmetic applications. We present novel sensing technology and a pipeline for capturing, modeling and analysis of the skin hydration phenomena and associated changes therein. By expanding sensing capabilities built into the SmartWatch sensor and combining them with advanced modeling and Machine Learning (ML) algorithms, we identified several important characteristics of photoplethysmography (PPG) signal and spectral sensitivity corresponding to dynamics of skin water content. In a hardware aspect, we newly propose the expansion of SmartWatch capabilities with InfraRed light sources equipped with wavelengths of 970 nm and 1450 nm. Evaluation of the accuracy and characteristics of PPG sensors has been performed with biomedical optics-based simulation framework using Monte Carlo simulations. We performed rigorous validation of the developed technology using experimental and clinical studies. The developed pipeline serves as a tool in the ongoing studies of the next generation of optical sensing technology.

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R² > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.