Best Electrode Locations for a Small Bipolar ECG Device: Signal Strength Analysis of Clinical Data

Sensitivity of Electrocardiogram on Electrode-Pair Locations for Wearable Devices: Computational Analysis of Amplitude and Waveform Distortion

An electrocardiogram (ECG) is used to observe the electrical activity of the heart via electrodes on the body surface. Recently, an ECG with fewer electrodes, such as a bipolar ECG in which two electrodes are attached to the chest, has been employed as wearable devices. However, the effect of different geometrical factors and electrode-pair locations on the amplitude and waveform of ECG signals remains unclear. In this study, we computationally evaluated the effects of body morphology, heart size and orientation, and electrode misalignment on ECG signals for 48 scenarios using 35 bipolar electrode pairs (1680 waveforms) with a dynamic time warping (DTW) algorithm. It was observed that the physique of the human body model predominantly affected the amplitude and waveform of the ECG signals. A multivariate analysis indicated that the heart-electrode distance and the solid angle of the heart from the electrode characterized the amplitude and waveform of the ECG signals, respectively. Furthermore, the electrode locations for less individual variability and less waveform distortion were close to the location of electrodes V2 and V3 in the standard 12-lead. These findings will facilitate the placement of ECG electrodes and interpretation of the measured ECG signals for wearable devices.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

ECG Signal Quality Assessments of a Small Bipolar Single-Lead Wearable Patch Sensor

PURPOSE

There is an increasing clinical interest in the adoption of small single-lead wearable ECG sensors for continuous cardiac monitoring. The purpose of this work is to assess ECG signal quality of such devices compared to gold standard 12-lead ECG.

METHODS

The ECG signal from a 1-lead patch was systematically compared to the 12-lead ECG device in thirty subjects to establish its diagnostic accuracy in terms of clinically relevant signal morphology, wave representation, fiducial markers and interval and wave duration. One minute ECG segments with good signal quality was selected for analysis and the features of ECG were manually annotated for comparative assessment.

RESULTS

The patch showed closest similarity based on correlation and normalized root-mean-square error to the standard ECG leads I, II, [Formula: see text] and [Formula: see text]. P-wave and QRS complexes in the patch showed sensitivity (Se) and positive predictive value (PPV) of at least 99.8% compared to lead II. T-wave representation showed Se and PPV of at least 99.9% compared to lead [Formula: see text] and [Formula: see text]. Mean errors for onset and offset of the ECG waves, wave durations, and ECG intervals were within 2 samples based on 125Hz patch ECG sampling frequency.

CONCLUSION

This study demonstrates the diagnostic capability with similar morphological representation and reasonable timing accuracy of ECG signal from a patch sensor compared to 12-lead ECG. The advantages and limitations of small bipolar single-lead wearable patch sensor compared to 12-lead ECG are discussed in the context of relevant differences in ECG signal for clinical applications.

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.

Neural Respiratory Drive Estimation in Respiratory sEMG with Cardiac Arrhythmias

Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively).

Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network

Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.

Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor.

Effect of Different ECG Leads on Estimated R-R Intervals and Heart Rate Variability Parameters

Heart rate and heart rate variability parameters provide important information on sympathetic and parasympathetic branches of autonomous nervous system. These parameters are usually extracted from electrocardiograms often measured between two electrodes and called an ECG lead. Besides systems intended only for heart rate measurement, ECG measurement devices employ several well-known lead systems including the standard 12-lead system, EASI lead system and Mason-Likar systems. Therefore, the first step is to select the appropriate lead for heart rate variability analysis. The appropriate electrode locations for single-lead measurement systems or the preferred measurement lead in multi-lead measurement are choices that the user needs to make when the heart rate variability is of interest. However, it has not been addressed in the literature, if the lead selection has an effect on the obtained HRV parameters. In this work, we characterized the amount of deviation of heart rate and heart rate variability parameters extracted from nine ECG leads, six from EASI leads and three modified limb leads. The results showed a deviation of 2.04, 2.88, 2.06 and 3.45 ms in SDNN, rMSSD, SD1 and SD2, respectively. A relative difference up to 10% was observed in HRV parameters for single signal frames. Additionally, the discrimination of the R-peaks by amplitudes was evaluated. The A-S lead appeared to have the best performance in all the tests.

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

Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart's electrical axis.

A Low-Power, Low-Cost Ingestible and Wearable Sensing Platform to Measure Medication Adherence and Physiological Signals

In this paper, we present a novel Digital Medicines program used for reviewing medication adherence. The program is comprised of an ingestible sensor embedded inside medication and a wearable sensor or patch worn on the skin of the patient. The ingestible sensor activates upon contact with gastric fluids and communicates information about the ingested drug to the patch. Adherence patterns and other physiological markers measured by the system are made available to patients, physicians, and caregivers via mobile and web interfaces. The paper focuses on the wearable sensor hardware and measurement features used to provide a more comprehensive view of the patient's health centered around and contextualized by adherence patterns. This is achieved using efficient, high-performance signal processing algorithms implemented on a low-power platform. Results from bench and clinical testing are presented to demonstrate the performance of adherence, heart rate, step counts, and body angle measurements.

Highly flexible, wearable, and disposable cardiac biosensors for remote and ambulatory monitoring

Contemporary cardiac and heart rate monitoring devices capture physiological signals using optical and electrode-based sensors. However, these devices generally lack the form factor and mechanical flexibility necessary for use in ambulatory and home environments. Here, we report an ultrathin (~1 mm average thickness) and highly flexible wearable cardiac sensor (WiSP) designed to be minimal in cost (disposable), light weight (1.2 g), water resistant, and capable of wireless energy harvesting. Theoretical analyses of system-level bending mechanics show the advantages of WiSP’s flexible electronics, soft encapsulation layers and bioadhesives, enabling intimate skin coupling. A clinical feasibility study conducted in atrial fibrillation patients demonstrates that the WiSP device effectively measures cardiac signals matching the Holter monitor, and is more comfortable. WiSP’s physical attributes and performance results demonstrate its utility for monitoring cardiac signals during daily activity, exertion and sleep, with implications for home-based care.

A highly flexible, low-power wearable sensor that harvests energy and monitors cardiac signals has been developed by Lee et al. The team was led by Dr. Roozbeh Ghaffari and co-workers at MC10 Inc. and Northwestern University’s Center for Bio-Integrated Electronics at the Simpson & Querrey Institute, in collaboration with the Massachusetts General Hospital and Tsinghua University. The novel wearable sensors measure cardiac signals comparable in signal fidelity to those achievable with expensive monitoring systems used in hospitals. Wearable health-care solutions are fundamentally changing the way we monitor our well-being at all times of the day, no matter whether we are asleep at home or busy at work. The sensors reported here are lightweight, inexpensive to manufacture, robust to everyday use, and capable of wireless data transmission and energy harvesting to and from a smartphone. The approach proved successful for measuring episodic electrocardiograms (ECG) and continuous heart rate signals with significantly higher patient comfort scores compared to standard Holter monitors in an initial pilot study conducted at the Massachusetts General Hospital (MGH).

Reconstruction of 12-lead ECG Using a Single-patch Device

Objectives: The aim of this study is to develop an optimal electrode system in the form of a small and wearable single-patch ECG monitoring device that allows for the faithful reconstruction of the standard 12-lead ECG.

Methods: The optimized universal electrode positions on the chest and the personalized transformation matrix were determined using linear regression as well as artificial neural networks (ANNs). A total of 24 combinations of 4 neighboring electrodes on 35 channels were evaluated on 19 subjects. Moreover, we analyzed combinations of three electrodes within the four-electrode combination with the best performance.

Results: The mean correlation coefficients were all higher than 0.95 in the case of the ANN method for the combinations of four neighboring electrodes. The reconstructions obtained using the three and four sensing electrodes showed no significant differences. The reconstructed 12-lead ECG obtained using the ANN method is better than that using the MLR method. Therefore, three sensing electrodes and one ground electrode (forming a square) placed below the clavicle on the left were determined to be suitable for ensuring good reconstruction performance.

Conclusions: Since the interelectrode distance was determined to be 5 cm, the suggested approach can be implemented in a single-patch device, which should allow for the continuous monitoring of the standard 12-lead ECG without requiring limb contact, both in daily life and in clinical practice.

Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation

Unobtrusive medical instrumentation is a key in continuous patient monitoring. To increase compliance, multi-functional sensor concepts and measurement sites different from gold-standards are used. In this work, we aim to combine both approaches. We focus on minimally spaced electrode positions with high signal correlations to gold-standards. We present twofold experimental data from six and eleven healthy volunteers and provide chest positions with individual correlations up to 0.83 ± 0.06 for ECG and 0.73 ± 0.28 for the respiratory frequency. Using a performance index, we assess positions with correlations up to 0.77 ± 0.12 for ECG and 0.65 ± 0.35 for the respiratory frequency with 24 mm electrode distance.

Development of a Wearable Cardiac Monitoring System for Behavioral Neurocardiac Training: A Usability Study

Background

Elevated blood pressure is one of the main risk factors for death globally. Behavioral neurocardiac training (BNT) is a complementary approach to blood pressure and stress management that is intended to exercise the autonomic reflexes, improve stress recovery, and lower blood pressure. BNT involves cognitive-behavioral therapy with a paced breathing technique and heart rate variability biofeedback. BNT is limited to in-clinic delivery and faces an accessibility barrier because of the need for clinical oversight and the use of complex monitoring tools.

Objective

The objective of this project was to design, develop, and evaluate a wearable electrocardiographic (ECG) sensor system for the delivery of BNT in a home setting.

Methods

The wearable sensor system, Beat, consists of an ECG sensor and a mobile app. It was developed iteratively using the principles of test-driven Agile development and user-centered design. A usability study was conducted at Toronto General Hospital to evaluate feasibility and user experience and identify areas of improvement.

Results

The Beatsensor was designed as a modular patch to be worn on the user’s chest and uses standard ECG electrodes. It streams a single-lead ECG wirelessly to a mobile phone using Bluetooth Low Energy. The use of small, low-power electronics, a low device profile, and a tapered enclosure allowed for a device that can be unobtrusively worn under clothing. The sensor was designed to operate with a mobile app that guides users through the BNT exercises to train them to a slow-paced breathing technique for stress recovery. The BNT app uses the ECG captured by the sensor to provide heart rate variability biofeedback in the form of a real-time heart rate waveform to complement and reinforce the impact of the training. Usability testing (n=6) indicated that the overall response to the design and user experience of the system was perceived positively. All participants indicated that the system had a positive effect on stress management and that they would use it at home. Areas of improvement were identified, which focused primarily on the delivery of training and education on BNT through the app.

Conclusions

The outcome of this project was a wearable sensor system to deliver BNT at home. The system has the potential to offer a complementary approach to blood pressure and stress management at home and reduce current accessibility barriers.

ECG synthesis from separate wearable bipolar electrodes

Compared to cabled ECG devices, the use of wearable patches to reconstruct ECG offers a more comfortable alternative for continuous monitoring, especially for patients at home. In this work, we investigate the feasibility of synthesizing a 3-lead ECG signal from 3 separate wearable and wireless patches. We also investigate the effect of their orientation on the synthesized signal. We conduct an experiment on healthy subjects and show the ability of this method to provide a similar ECG signal to the reference, in terms of matching the overall signal pattern. However, a more detailed study would be needed in order to investigate the ability of this method to identify critical conditions for vulnerable subjects.

An optimized lead system for long-term esophageal electrocardiography

Long-term electrocardiography (ECG) featuring adequate atrial and ventricular signal quality is highly desirable. Routinely used surface leads are limited in atrial signal sensitivity and recording capability impeding complete ECG delineation, i.e. in the presence of supraventricular arrhythmias. Long-term esophageal ECG might overcome these limitations but requires a dedicated lead system and recorder design. To this end, we analysed multiple-lead esophageal ECGs with respect to signal quality by describing the ECG waves as a function of the insertion level, interelectrode distance, electrode shape and amplifier's input range. The results derived from clinical data show that two bipolar esophageal leads, an atrial lead with short (15 mm) interelectrode distance and a ventricular lead with long (80 mm) interelectrode distance provide non-inferior ventricular signal strength and superior atrial signal strength compared to standard surface lead II. High atrial signal slope in particular is observed with the atrial esophageal lead. The proposed esophageal lead system in combination with an increased recorder input range of ±20 mV minimizes signal loss due to excessive electrode motion typically observed in esophageal ECGs. The design proposal might help to standardize long-term esophageal ECG registrations and facilitate novel ECG classification systems based on the independent detection of ventricular and atrial electrical activity.

Two proximal skin electrodes--a respiration rate body sensor

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor's position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

A preliminary study of the effect of electrode placement in order to define a suitable location for two electrodes and obtain sufficiently reliable ECG signals when monitoring with wireless system

Most countries face high and increasing rates of cardiovascular disease. Each year, heart disease kills more Americans than cancer. Therefore, there has been a promising market for portable ECG equipment and it is increasing. To use portable ECG measuring devices, it is essential to define a suitable location for the measuring as we need to reduced electrode size and distance. This research proposes to study how the inter-electrode distance affects the signal and how the electrode pair should be placed on the chest in order to obtain a sufficiently reliable ECG signal to detect heart arrhythmias in any environment, such as home or work. Therefore, we developed a compact, portable patch type ambulatory ECG monitoring system, Heart Tracker, using a microprocessor for preliminary study of signal analysis. To optimize the electrode arrangement in wireless environment, we compared HT and standard 12 lead with changing electrode position.

Synthesis of the 12-lead electrocardiogram from differential leads

A new approach is proposed for synthesizing the standard 12-lead ECG from three differential leads formed by pairs of proximal electrodes on the body surface. The method is supported by a statistical analysis that gives the best personalized positions of electrodes. The measurements from multichannel ECGs were used to calculate the differential leads. Our algorithm searches for optimal differential leads and the corresponding personalized transformation matrix that is used to synthesize the standard 12-lead ECG. The algorithm has been evaluated on 99 multichannel ECGs measured on 30 healthy subjects and 35 patients scheduled for elective cardiac surgery. It is shown that the algorithm significantly outperforms the synthesis based on the EASI lead system with medians of correlation coefficients greater than 0.954 for all 12 standard leads. To determine the optimal number of differential leads, the syntheses for two, three, and four differential leads were calculated. The analysis shows that 3 is the optimal number of differential leads for practical applications. Because of the proximity of the differential electrodes, the proposed approach offers an opportunity for the synthesis of a standard 12-lead ECG with wireless electrodes.

New precordial bipolar electrocardiographic leads for detecting left ventricular hypertrophy

BACKGROUND

Novel small and wearable electrocardiogram (ECG) devices offer new means of recording cardiac activity in different applications. Our objective was to evaluate the performance of closely separated (6 cm) bipolar leads in differentiating subjects with left ventricular hypertrophy (LVH) from healthy subjects.

METHODS

The material contained body surface ECG of 236 healthy and 116 LVH subjects. A total of 36 vertical, 30 horizontal, and 66 diagonal bipolar leads located on the anterior thorax were analyzed. The QRS amplitudes were calculated, and the leads' overall diagnostic performance was assessed by receiver operating characteristic (ROC) analysis.

RESULTS

The best overall diagnostic performances were obtained from 2 areas: one near the precordial electrodes of standard leads V(1) to V(3) and the other on lower anterior thorax. Vertical and diagonal bipolar leads located at lower anterior thorax provided the highest ROC areas (≥0.79). These bipolar leads also provided similar sensitivities than the traditional Sokolow-Lyon method.

CONCLUSION

The new short distance vertical and diagonal bipolar leads are efficient in discriminating subjects with LVH from healthy subjects based on QRS amplitude.