Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules based on the LANS Method

Development of Machine Learning Model for VO2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO2max) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland-Altman plot of measured and estimated VO2max, the VO2max values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: -0.33 mL·kg-1·min-1, bias: 0.30 mL·kg-1·min-1, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO2max and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.

Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy

Wearable wireless electrocardiographic (ECG) monitoring is well-proven for arrythmia detection, but ischemia detection accuracy is not well-described. We aimed to assess the agreement of ST-segment deviation from single- versus 12-lead ECG and their accuracy for the detection of reversible ischemia. Bias and limits of agreement (LoA) were calculated between maximum deviations in ST segments from single- and 12-lead ECG during ⁸²Rb PET-myocardial cardiac stress scintigraphy. Sensitivity and specificity for reversible anterior-lateral myocardial ischemia detection were assessed for both ECG methods, using perfusion imaging results as a reference. Out of 110 patients included, 93 were analyzed. The maximum difference between single- and 12-lead ECG was seen in II (-0.019 mV). The widest LoA was seen in V5, with an upper LoA of 0.145 mV (0.118 to 0.172) and a lower LoA of -0.155 mV (-0.182 to -0.128). Ischemia was seen in 24 patients. Single-lead and 12-lead ECG both had poor accuracy for the detection of reversible anterolateral ischemia during the test: single-lead ECG had a sensitivity of 8.3% (1.0-27.0%) and specificity of 89.9% (80.2-95.8%), and 12-lead ECG a sensitivity of 12.5% (3.0-34.4%) and a specificity of 91.3% (82.0-96.7%). In conclusion, agreement was within predefined acceptable criteria for ST deviations, and both methods had high specificity but poor sensitivity for the detection of anterolateral reversible ischemia. Additional studies must confirm these results and their clinical relevance, especially in the light of the poor sensitivity for detecting reversible anterolateral cardiac ischemia.

Arrhythmia and Heart Rate Variability during Long Interdialytic Periods in Patients on Maintenance Hemodialysis: Prospective Observational Cohort Study

Sudden cardiac death among hemodialysis patients is related to the hemodialysis schedule. Mortality is highest within 12 h before and after the first hemodialysis sessions of a week. We investigated the association of arrhythmia occurrence and heart rate variability (HRV) using an electrocardiogram (ECG) monitoring patch during the long interdialytic interval in hemodialysis patients. This was a prospective observational study with 55 participants on maintenance hemodialysis for at least six months. A patch-type ECG monitoring device was applied to record arrhythmia events and HRV during 72 h of a long interdialytic period. Forty-nine participants with sufficient ECG data out of 55 participants were suitable for the analysis. The incidence of supraventricular tachycardia and ventricular tachycardia did not significantly change over time. The square root of the mean squared differences of successive NN intervals (RMSSD), the proportion of adjacent NN intervals differing by >50 ms (pNN50), and high-frequency (HF) increased during the long interdialytic interval. The gap in RMSSD, pNN50, HF, and the low-frequency/high-frequency (LF/HF) ratio between patients with and without significant arrhythmias increased significantly over time during the long interdialytic interval. The daily changes in RMSSD, pNN50, HF, and the LF/HF ratio were more prominent in patients without significant arrhythmias than in those with significant arrhythmias. The electrolyte fluctuation between post-hemodialysis and subsequent pre-hemodialysis was not considered in this study. The study results suggest that the decreased autonomic response during interdialytic periods in dialysis patients is associated with poor cardiac arrhythmia events.

Non-Standard Electrode Placement Strategies for ECG Signal Acquisition

BACKGROUND

Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making.

METHODS

In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves.

RESULTS

Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results.

CONCLUSIONS

The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.

DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification

In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation

The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.

Effect of the recording condition on the quality of a single-lead electrocardiogram

Although many wearable single-lead electrocardiogram (ECG) monitoring devices have been developed, information regarding their ECG quality is limited. This study aimed to evaluate the quality of single-lead ECG in healthy subjects under various conditions (body positions and motions) and in patients with arrhythmias, to estimate requirements for automatic analysis, and to identify a way to improve ECG quality by changing the type and placement of electrodes. A single-lead ECG transmitter was placed on the sternum with a pair of electrodes, and ECG was simultaneously recorded with a conventional Holter ECG in 12 healthy subjects under various conditions and 35 patients with arrhythmias. Subjects with arrhythmias were divided into sinus rhythm (SR) and atrial fibrillation (AF) groups. ECG quality was assessed by calculating the sensitivity and positive predictive value (PPV) of the visual detection of QRS complexes (vQRS), automatic detection of QRS complexes (aQRS), and visual detection of P waves (vP). Accuracy was defined as a 100% sensitivity and PPV. We also measured the amplitude of the baseline, P wave, and QRS complex, and calculated the signal-to-noise ratio (SNR). We then focused on aQRS and estimated thresholds to obtain an accurate aQRS in more than 95% of the data. Finally, we sought to improve ECG quality by changing electrode placement using offset-type electrodes in 10 healthy subjects. The single-lead ECG provided 100% accuracy for vQRS, 87% for aQRS, and 74% for vP in healthy subjects under various conditions. Failure for accurate detection occurred in several motions in which the baseline amplitude was increased or in subjects with low QRS or P amplitude, resulting in low SNR. The single-lead ECG provided 97% accuracy for vQRS, 80% for aQRS in patients with arrhythmias, and 95% accuracy for vP in the SR group. The AF group showed higher baseline amplitude than the SR group (0.08 mV vs. 0.02 mV, P < 0.01) but no significant difference in accuracy for aQRS (79% vs. 81%, P = 1.00). The thresholds to obtain an accurate aQRS were a QRS amplitude > 0.42 mV and a baseline amplitude < 0.20 mV. The QRS amplitude was significantly influenced by electrode placement and body position (P < 0.01 for both, two-way analysis of variance), and the maximum reduction by changing body position was estimated as 30% compared to the sitting posture. The QRS amplitude significantly increased when the inter-electrode distance was extended vertically (1.51 mV for vertical extension vs. 0.93 mV for control, P < 0.01). The single-lead ECG provided at least 97% accuracy for vQRS, 80% for aQRS, and 74% for vP. To obtain stable aQRS in any body positions, a QRS amplitude > 0.60 mV and a baseline amplitude < 0.20 mV were required in the sitting posture considering the reduction induced by changing body position. Vertical extension of the inter-electrode distance increased the QRS amplitude.

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

Validation of Adhesive Single-Lead ECG Device Compared with Holter Monitoring among Non-Atrial Fibrillation Patients

There are few reports on head-to-head comparisons of electrocardiogram (ECG) monitoring between adhesive single-lead and Holter devices for arrhythmias other than atrial fibrillation (AF). This study aimed to compare 24 h ECG monitoring between the two devices in patients with general arrhythmia. Twenty-nine non-AF patients with a workup of pre-diagnosed arrhythmias or suspicious arrhythmic episodes were evaluated. Each participant wore both devices simultaneously, and the cardiac rhythm was monitored for 24 h. Selective ECG parameters were compared between the two devices. Two cardiologists independently compared the diagnoses of each device. The two most frequent monitoring indications were workup of premature atrial contractions (41.4%) and suspicious arrhythmia-related symptoms (37.9%). The single-lead device had a higher noise burden than the Holter device (0.04 ± 0.05% vs. 0.01 ± 0.01%, p = 0.024). The number of total QRS complexes, ventricular ectopic beats, and supraventricular ectopic beats showed an excellent degree of agreement between the two devices (intraclass correlation coefficients = 0.991, 1.000, and 0.987, respectively). In addition, the minimum/average/maximum heart rates showed an excellent degree of agreement. The two cardiologists made coherent diagnoses for all 29 participants using both monitoring methods. In conclusion, the single-lead adhesive device could be an acceptable alternative for ambulatory ECG monitoring in patients with general arrhythmia.