QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3

AIMS

Nonalcoholic fatty liver disease (NAFLD) is a multisystem disease that can trigger the metabolic syndrome. Early prevention and treatment of NAFLD is still a huge challenge for patients and clinicians. The aim of this study was to develop and validate machine learning (ML)-based predictive models. The model with optimal performance would be developed as a set of simple arithmetic tools for predicting the risk of NAFLD individually.

METHODS

Statistical analyses were performed in 2428 individuals extracted from the National Health and Nutrition Examination Survey (NHANES, cycle 2017-2020.3) database. Feature variables were selected by the least absolute shrinkage and selection operator (LASSO) regression. Seven ML algorithms, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), K-nearest neighbor (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to construct models based on the feature variables and evaluate their performance. The model with the best performance was transformed into a diagnostic predictive nomogram (DPN). The DPN was developed into an online calculator and an Excel algorithm tool. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and subgroup analyses were used to compare and assess the predictive abilities of the DPN and six existing NAFLD predictive models, including the ZJU index, the hepatic steatosis index (HSI), the triglyceride-glucose index (TyG), the Framingham steatosis index (FSI), the fatty liver index (FLI), and the visceral adiposity index (VAI).

RESULTS

Among the 2428 participants, the prevalence of NAFLD was 47.45%. LASSO regression identified eight variables from 39 variables, including body mass index (BMI), waist circumference (WC), alanine aminotransferase (ALT), triglyceride (TG), diabetes, hypertension, uric acid (UA), and race. Among the models constructed by the seven algorithms mentioned above, the LR-based model performed the best, demonstrating outstanding performance in terms of area under the curve (AUC, 0.823), accuracy (0.754), precision (0.768), specificity (0.804), and positive predictive value (0.768). It was then transformed into the DPN, which was successfully developed as an online calculator and an Excel algorithm tool. The diagnostic accuracy (AUC 0.856, 95% confidence interval (CI) 0.839-0.874, and AUC 0.823, 95% CI 0.793-0.854, respectively) and net clinical benefit of DPN in the training and validation sets were superior to those of the ZJU, HSI, TyG, FSI, FLI, and VAI. The results were maintained in subgroup analyses.

CONCLUSIONS

The LR model based on ML was developed, exhibiting good performance. DPN can be used as an individualized tool for rapid detection of NAFLD.

A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG

Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model's performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.

Deep residual 2D convolutional neural network for cardiovascular disease classification

Cardiovascular disease (CVD) continues to be a major global health concern, underscoring the need for advancements in medical care. The use of electrocardiograms (ECGs) is crucial for diagnosing cardiac conditions. However, the reliance on professional expertise for manual ECG interpretation poses challenges for expanding accessible healthcare, particularly in community hospitals. To address this, there is a growing interest in leveraging automated and AI-driven ECG analysis systems, which can enhance diagnostic accuracy and efficiency, making quality cardiac care more accessible to a broader population. In this study, we implemented a novel deep two-dimensional convolutional neural network (2D-CNN) on a dataset of PTB-XL for cardiac disorder detection. The studies were performed on 2, 5, and 23 classes of cardiovascular diseases. The our network in classifying healthy/sick patients achived an AUC of 95% and an average accuracy of 87.85%. In 5-classes classification, our model achieved an AUC of 93.46% with an average accuracy of 89.87%. In a more complex scenario involving classification into 23 different classes, the model achieved an AUC of 92.18% and an accuracy of 96.88%. According to the experimental results, our model obtained the best classification result compared to the other methods based on the same public dataset. This indicates that our method can aid healthcare professionals in the clinical analysis of ECGs, offering valuable assistance in diagnosing CVD and contributing to the advancement of computer-aided diagnosis technology.

Enhancing P-wave localization for accurate detection of second-degree and third-degree atrioventricular conduction blocks

Objective.This paper tackles the challenge of accurately detecting second-degree and third-degree atrioventricular block (AVB) in electrocardiogram (ECG) signals through automated algorithms. The inaccurate detection of P-waves poses a difficulty in this process. To address this limitation, we propose a reliable method that significantly improves the performances of AVB detection by precisely localizing P-waves.Approach.Our proposed P-WaveNet utilized an attention mechanism to extract spatial and temporal features, and employs a bidirectional long short-term memory module to capture inter-temporal dependencies within the ECG signal. To overcome the scarcity of data for second-degree and third-degree AVB (2AVB,3AVB), a mathematical approach was employed to synthesize pseudo-data. By combining P-wave positions identified by the P-WaveNet with key medical features such as RR interval rhythm and PR intervals, we established a classification rule enabling automatic AVB detection.Main results. The P-WaveNet achieved an F1 score of 93.62% and 91.42% for P-wave localization on the QT Dataset and Lobachevsky University dataset datasets, respectively. In the BUTPDB dataset, the F1 scores for P-wave localization in ECG signals with 2AVB and 3AVB were 98.29% and 62.65%, respectively. Across two independent datasets, the AVB detection algorithm achieved F1 scores of 83.33% and 84.15% for 2AVB and 3AVB, respectively.Significance.Our proposed P-WaveNet demonstrates accurate identification of P-waves in complex ECGs, significantly enhancing AVB detection efficacy. This paper's contributions stem from the fusion of medical expertise with data augmentation techniques and ECG classification. The proposed P-WaveNet demonstrates potential clinical applicability.

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.

Heart patient health monitoring system using invasive and non-invasive measurement

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach

BACKGROUND

The prevalence of suicidal ideation has become an urgent issue, particularly among adolescents. The primary objective of this research is to determine the prevalence of suicidal ideation among students in the southern region of Bangladesh and to predict this phenomenon using machine learning (ML) models.

METHODS

The data collection process involved using a simple random sampling technique to gather information from university students located in the southern region of Bangladesh during the period spreading from April 2022 to June 2022. Upon accounting for missing values and non-response rates, the ultimate sample size was determined to be 584, with 51.5 % of participants identifying as male and 48.5 % female.

RESULTS

A significant proportion of students, precisely 19.9 %, reported experiencing suicidal ideation. Most participants were female (77 %) and unmarried (78 %). Within the machine learning (ML) framework, KNN exhibited the highest accuracy score of 91.45 %. In addition, the Random Forest (RF), and Categorical Boosting (CatBoost) algorithms exhibited comparable levels of accuracy, achieving scores of 90.60 and 90.59 respectively.

LIMITATIONS

Using a cross-sectional design in research limits the ability to establish causal relationships.

CONCLUSION

Mental health practitioners can employ the KNN model alongside patients' medical histories to detect those who may be at a higher risk of attempting suicide. This approach enables healthcare professionals to take appropriate measures, such as counselling, encouraging regular sleep patterns, and addressing depression and anxiety, to prevent suicide attempts.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

BACKGROUND

No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

METHODS

In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%.

RESULTS

From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets.

CONCLUSION

This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

Multifractal detrended fluctuation analysis of insole pressure sensor data to diagnose vestibular system disorders

The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long walking time. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short walk. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an average accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.

Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual's walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts.

METHODS

We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients.

RESULTS

Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values.

CONCLUSIONS

Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network

Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in the model's prediction stream using a basic Salt and Pepper filter, as well as, tasks that use domain-specific thresholds, including a minimum QRS size, and a minimum or maximum R-R distance. These thresholds were found to vary among QRS-detection studies and empirically determined for the target dataset, which may have implications if the target dataset differs such as the drop of performance in unknown test datasets. Moreover, these studies, in general, fail to identify the relative strengths of deep-learning models and the post-processing to weigh them appropriately. This study identifies the domain-specific post-processing, as found in the QRS-detection literature, as three steps based on the required domain knowledge. It was found that the use of minimal domain-specific post-processing is often sufficient for most of the cases and the use of additional domain-specific refinement ensures superior performance, however, it makes the process biased towards the training data and lacks generalisability. As a remedy, a domain-agnostic automated post-processing is introduced where a separate recurrent neural network (RNN)-based model learns required post-processing from the output generated from a QRS-segmenting deep learning model, which is, to the best of our knowledge, the first of its kind. The RNN-based post-processing shows superiority over the domain-specific post-processing for most of the cases (with shallow variants of the QRS-segmenting model and datasets like TWADB) and lags behind for others but with a small margin ( ≤ 2%). The consistency of the RNN-based post-processor is an important characteristic which can be utilised in designing a stable and domain agnostic QRS detector.

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

QRS detection and classification in Holter ECG data in one inference step

While various QRS detection and classification methods were developed in the past, the Holter ECG data acquired during daily activities by wearable devices represent new challenges such as increased noise and artefacts due to patient movements. Here, we present a deep-learning model to detect and classify QRS complexes in single-lead Holter ECG. We introduce a novel approach, delivering QRS detection and classification in one inference step. We used a private dataset (12,111 Holter ECG recordings, length of 30 s) for training, validation, and testing the method. Twelve public databases were used to further test method performance. We built a software tool to rapidly annotate QRS complexes in a private dataset, and we annotated 619,681 QRS complexes. The standardised and down-sampled ECG signal forms a 30-s long input for the deep-learning model. The model consists of five ResNet blocks and a gated recurrent unit layer. The model's output is a 30-s long 4-channel probability vector (no-QRS, normal QRS, premature ventricular contraction, premature atrial contraction). Output probabilities are post-processed to receive predicted QRS annotation marks. For the QRS detection task, the proposed method achieved the F1 score of 0.99 on the private test set. An overall mean F1 cross-database score through twelve external public databases was 0.96 ± 0.06. In terms of QRS classification, the presented method showed micro and macro F1 scores of 0.96 and 0.74 on the private test set, respectively. Cross-database results using four external public datasets showed micro and macro F1 scores of 0.95 ± 0.03 and 0.73 ± 0.06, respectively. Presented results showed that QRS detection and classification could be reliably computed in one inference step. The cross-database tests showed higher overall QRS detection performance than any of compared methods.

Machine learning algorithms identifying the risk of new-onset ACS in patients with type 2 diabetes mellitus: A retrospective cohort study

Background

In recent years, the prevalence of type 2 diabetes mellitus (T2DM) has increased annually. The major complication of T2DM is cardiovascular disease (CVD). CVD is the main cause of death in T2DM patients, particularly those with comorbid acute coronary syndrome (ACS). Although risk prediction models using multivariate logistic regression are available to assess the probability of new-onset ACS development in T2DM patients, none have been established using machine learning (ML).

Methods

Between January 2019 and January 2020, we enrolled 521 T2DM patients with new-onset ACS or no ACS from our institution's medical information recording system and divided them into a training dataset and a testing dataset. Seven ML algorithms were used to establish models to assess the probability of ACS coupled with 5-cross validation.

Results

We established a nomogram to assess the probability of newly diagnosed ACS in T2DM patients with an area under the curve (AUC) of 0.80 in the testing dataset and identified some key features: family history of CVD, history of smoking and drinking, aspartate aminotransferase level, age, neutrophil count, and Killip grade, which accelerated the development of ACS in patients with T2DM. The AUC values of the seven ML models were 0.70-0.96, and random forest model had the best performance (accuracy, 0.89; AUC, 0.96; recall, 0.83; precision, 0.91; F1 score, 0.87).

Conclusion

ML algorithms, especially random forest model (AUC, 0.961), had higher performance than conventional logistic regression (AUC, 0.801) for assessing new-onset ACS probability in T2DM patients with excellent clinical and diagnostic value.

A Human Defecation Prediction Method Based on Multi-Domain Features and Improved Support Vector Machine

The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM) using bowel sound as the original signal. The method includes three stages: multi-domain features extraction, feature optimization, and defecation prediction. In the stage of multi-domain features extraction, statistical analysis, fast Fourier transform (FFT), and wavelet packet transform are used to extract feature information in the time domain, frequency domain, and time-frequency domain. The symmetry of the bowel sound signal in the time domain, frequency domain, and time-frequency domain will change when the human has the urge to defecate. In the feature optimization stage, the Fisher Score (FS) algorithm is introduced to select meaningful and sensitive features according to the importance of each feature, aiming to remove redundant information and improve computational efficiency. In the stage of defecation prediction, SVM is optimized by the gray wolf optimization (GWO) algorithm to realize human defecation prediction. Finally, experimental analysis of the bowel sound data collected during the study is carried out. The experimental result shows that the proposed method could achieve an accuracy of 92.86% in defecation prediction, which proves the effectiveness of the proposed method.

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.

To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach

The large conductance voltage- and Ca2+-activated K+ channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It is well known that in the presence of Nar the open state probability (pop) of mitoBK channels significantly increases. Nevertheless, the molecular mechanism of the mitoBK-Nar interactions remains still unrevealed. It is also not known whether the effects of naringenin administration on conformational dynamics can resemble those which are exerted by the other channel-activating stimuli. In aim to answer this question, we examine whether the dwell-time series of mitoBK channels which were obtained at different voltages and Nar concentrations (yet allowing to reach comparable pops) are discernible by means of artificial intelligence methods, including k-NN and shapelet learning. The obtained results suggest that the structural complexity of the gating dynamics is shaped both by the interaction of channel gate with the voltage sensor (VSD) and the Nar-binding site. For a majority of data one can observe stimulus-specific patterns of channel gating. Shapelet algorithm allows to obtain better prediction accuracy in most cases. Probably, because it takes into account the complexity of local features of a given signal. About 30% of the analyzed time series do not sufficiently differ to unambiguously distinguish them from each other, which can be interpreted in terms of the existence of the common features of mitoBK channel gating regardless of the type of activating stimulus. There exist long-range mutual interactions between VSD and the Nar-coordination site that are responsible for higher levels of Nar-activation (Δpop) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature.

The role of artificial intelligence in paediatric neuroradiology

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients' outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the "unhealthy" and "healthy" classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients' class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the "healthy" (good outcome) or "unhealthy" (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

Arrhythmia detection and classification using ECG and PPG techniques: a review

Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.

TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people's learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people's learning styles.

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Application of Artificial Intelligence in Cardiovascular Medicine

The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine. © 2021 American Physiological Society. Compr Physiol 11:1-12, 2021.

Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines

Background

As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of “At Risk” CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates.

Results

A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score.

Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Cardiac Severity Classification Using Pre Trained Neural Networks

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficiency. This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN), by providing a pre-processed ECG signal as an excitation. The modified ECG signal is obtained through the combination of EMD (Empirical Mode Decomposition) and DWT (Discrete Wavelet Transform). In this proposed method, the input signal is first decomposed into the Intrinsic Mode Functions (IMF's) and the first three IMF's are combined to obtain a modified partially denoted ECG sample and then DWT is used to obtain an improved denoised signal. This pre-processed signal is classified using the Neural Network architecture. For the EMD approach, the ECG-based EMD-DWT signal provides improved classification accuracy of 67, 0762 percent, 90, 4305 percent for the DWT approach, and 95,0797 percent for the proposed technique. The methodology is applied to the MIT-BIH database and, in terms of classification accuracy, is found to be higher than the different methodologies.

Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results

(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca2+-activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK). The characteristic electrophysiological features of the mitoBK channels are relatively high single-channel conductance (ca. 300 pS) and types of activating and deactivating stimuli. Nevertheless, depending on the isoformal composition of mitoBK channels in a given membrane patch and the type of auxiliary regulatory subunits (which can be co-assembled to the mitoBK channel protein) the characteristics of conformational dynamics of the channel protein can be altered. Consequently, the individual features of experimental series describing single-channel activity obtained by patch-clamp method can also vary. (2) Methods: Artificial intelligence approaches (deep learning) were used to classify the patch-clamp outputs of mitoBK activity from different cell types. (3) Results: Application of the K-nearest neighbors algorithm (KNN) and the autoencoder neural network allowed to perform the classification of the electrophysiological signals with a very good accuracy, which indicates that the conformational dynamics of the analyzed mitoBK channels from different cell types significantly differs. (4) Conclusion: We displayed the utility of machine-learning methodology in the research of ion channel gating, even in cases when the behavior of very similar microbiosystems is analyzed. A short excerpt from the patch-clamp recording can serve as a “fingerprint” used to recognize the mitoBK gating dynamics in the patches of membrane from different cell types.

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

Platform for Analysis and Labeling of Medical Time Series

Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations-(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis.

Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late.

Artificial intelligence in the diagnosis of cardiovascular disease

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.

Improving protein function prediction using protein sequence and GO-term similarities

MOTIVATION

Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict.

RESULTS

We compare proteins using a sequence similarity profile (SSP) to a set of annotated training proteins. We introduce two new LSDR methods, one based on the structure of the GO, and one based on semantic similarity of terms. We show that these LSDR methods, as well as three existing ones, improve the Critical Assessment of Functional Annotation performance of several function prediction algorithms. Cross-validation experiments on Arabidopsis thaliana proteins pinpoint the superiority of our GO-aware LSDR over generic LSDR. Our experiments on A.thaliana proteins show that the SSP representation in combination with a kNN classifier outperforms state-of-the-art and baseline methods in terms of cross-validated F-measure.

AVAILABILITY AND IMPLEMENTATION

Source code for the experiments is available at https://github.com/stamakro/SSP-LSDR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method

Background

It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.

Results

In this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons’ pulse firing signals are improved or not, we can reveal the information flows distribution of BNNs. Thus, the functional connections from presynaptic neurons can be identified from the obtained network information flows. To verify the effectiveness of the proposed method, the Nonlinear Granger Causality Identification Method (NGCIM) is applied to the network structure discovery processes of Spiking Neural Networks (SNN). SNN is a simulation model based on an Integrate-and-Fire mechanism. By network simulations, the multi-channel neuronal pulse sequence data of the SNNs can be used to reversely identify the synaptic connections and strengths of the SNNs.

Conclusions

The identification results show: for 2–6 nodes small-scale neural networks, 20 nodes medium-scale neural networks, and 100 nodes large-scale neural networks, the identification accuracy of NGCIM with the Gaussian kernel function was 100%, 99.64%, 98.64%, 98.37%, 98.31%, 84.87% and 80.56%, respectively. The identification accuracies were significantly higher than those of a traditional Linear Granger Causality Identification Method with the same network sizes. Thus, with an accumulation of the data obtained by the existing measurement methods, such as Electroencephalography, functional Magnetic Resonance Imaging, and Multi-Electrode Array, the NGCIM can be a promising network modeling method to infer the functional connective maps of BNNs.

Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias

In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTM simulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.