Real-time Electrocardiogram Signal Research Articles

A hydrophobic graft-modified PVA hydrogel by michael addition joint with gelation strategy for underwater cardiac sensing

The rapid development of global big health and AI technology has made electrocardiogram (ECG) monitoring in all scenarios more important. While real-time ECG monitoring in the air is easy to realize, underwater ECG monitoring is still challenging for general hydrogels. Herein, we prepared a hydrogel with underwater viscosity, biocompatibility, and excellent mechanical properties by hydrophobic graft-modified of PVA (POV) hydrogel and used it to prepare electrodes for underwater ECG monitoring. The POV hydrogel is very flexible and co-deforms easily with the skin, with a Young's modulus as low as 0.03 MPa and underwater adhesion strength up to 11 KPa. The hydrophobic chain segments within the POV hydrogel give the hydrophobic gel excellent stability, adhesion, and ionic conductivity given by the free ionic groups within the POV hydrogel enable the POV hydrogel to detect and transmit physiological electrical signals. Compared to commercial gel electrodes, this POV electrode has better adhesion, conductivity and stability underwater. The POV gel electrodes can effectively collect real-time ECG signals in the air and underwater. Therefore, this study effectively broaden the application of POV hydrogels in ECG biosensors for underwater cardiac sensing.

REAL-TIME PROCESSING STAGES OF ELECTROCARDIOGRAM SIGNAL: A REVIEW

Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.

Design and Development of an Android-based Remote Cardiac Monitoring Device for Continuous Real-time ECG Signal Acquisition, Transmission, and Analysis

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, necessitating innovative solutions for early detection and continuous monitoring. This research aims to develop an Android-based remote cardiac monitoring device for real-time electrocardiogram (ECG) signal acquisition, transmission, and analysis. The system comprises hardware for acquiring ECG signals, algorithms for processing and machine learning models for anomaly classification. The hardware unit captures ECG data using electrodes and sensors. The signals are filtered, processed, and transmitted to the cloud infrastructure enabling real-time monitoring and analysis. Machine learning models including support vector machines, ensemble methods and Artificial neural networks are trained on ECG datasets to classify signals and detect cardiac abnormalities. Comprehensive testing validates the system's capabilities in real-time signal acquisition, processing, anomaly detection and data transmission. The integration of hardware, algorithms and machine learning enables round-the-clock monitoring of cardiac activity, facilitating prompt interventions and improved patient outcomes. This affordable and user-friendly system demonstrates potential for enhanced accessibility and effectiveness of preventive cardiac care.

A 43.5dB Gain Unipolar a-IGZO TFT Amplifier with Parallel Bootstrap Capacitor for Bio-signals Sensing Applications.

In this paper, a high gain amplifier with phase compensation loop is presented. A structure of parallel gate cross-coupled transistors to both ends of differential pair drain and source is designed to improves the load impedance, which obtains sufficient gain and further reduces power consumption. A novel capacitor bootstrap load circuit is proposed. The capacitor bootstrap topology is constructed by the drain source resistance of the transistor working in the cut-off region, where the gate source parasitic capacitor of the transistor is in parallel with the bootstrap capacitor rather than the existing series structure, thereby only a small bootstrap capacitor is required. By avoiding the use of large capacitors, chip area can be effectively reduced without compromising performance such as gain and bandwidth. The amplifier is fabricated using 10-μm n-type a-IGZO TFT technology. Measurement results show that the proposed amplifier achieves a voltage gain of 43.5dB and a common mode rejection ratio of 61.2dB while maintaining low power consumption. The amplifier also exhibits a -3dB bandwidth covering 0.4~2.1KHz, encompassing major bioelectric frequency bands. A real-time ECG signal was successfully captured using the fabricated TFT amplifier and gel electrodes. It has great potential in flexible sensing and acquisition applications such as electro cardiogram (ECG), electro encephalogram (EEG), pulse detection, and other wearable applications.

Data-driven Energy-efficient Adaptive Sampling Using Deep Reinforcement Learning

This article presents a resource-efficient adaptive sampling methodology for classifying electrocardiogram (ECG) signals into different heart rhythms. We present our methodology in two folds: ( i ) the design of a novel real-time adaptive neural network architecture capable of classifying ECG signals with different sampling rates and ( ii ) a runtime implementation of sampling rate control using deep reinforcement learning (DRL). By using essential morphological details contained in the heartbeat waveform, the DRL agent can control the sampling rate and effectively reduce energy consumption at runtime. To evaluate our adaptive classifier, we use the MIT-BIH database and the recommendation of the AAMI to train the classifiers. The classifier is designed to recognize three major types of arrhythmias, which are supraventricular ectopic beats (SVEB), ventricular ectopic beats (VEB), and normal beats (N). The performance of the arrhythmia classification reaches an accuracy of 97.2% for SVEB and 97.6% for VEB beats. Moreover, the designed system is 7.3× more energy-efficient compared to the baseline architecture, where the adaptive sampling rate is not utilized. The proposed methodology can provide reliable and accurate real-time ECG signal analysis with performances comparable to state-of-the-art methods. Given its time-efficient, low-complexity, and low-memory-usage characteristics, the proposed methodology is also suitable for practical ECG applications, in our case for arrhythmia classification, using resource-constrained devices, especially wearable healthcare devices and implanted medical devices.

ECG R-Wave Detection and Its Application in Left Ventricular Assist Device

Heart failure is one of the major diseases endangering human life. As a transitional support treatment before heart transplantation, the left ventricular assist device (LVAD) has significantly improved the quality of life and survival rate of patients with end-stage heart failure. In the automatic detection of electrocardiogram (ECG), the detection of QRS wave groups is the most critical aspect, which affects the correctness and accuracy of subsequent data analysis and processing. The paper used the data from the MIT-BIH database and the data collected from human ECG as the original number of samples for further processing. It processed the data through a high-order finite impulse response (FIR) low-pass filter and Shannon energy algorithm, and added the use of a high-order adaptive median filter algorithm on a field programmable gate array (FPGA) to minimize the noise of the processed real-time data. Finally, the tested ECG R-wave was used to drive the LVAD. After successful simulation by MATLAB and Modelsim software, the scheme of the real-time ECG signal controlling blood pump system was realized on the FPGA platform. The experiment showed that the method proposed could accurately extract the R-wave and control the LVAD to pump blood simultaneously.

Chaotic encryption of real-time ECG signal in embedded system for secure telemedicine

Security in telemedicine has been of great interest in recent years due to the personal and confidential information that is used, since it is sent or stored through insecure channels, which generates a security problem such in telemetry, where biomedical signals (ECG, EEG, SpO2) are used for diagnosis of diseases. In this work, a chaos-based cryptographic algorithm is proposed using two-dimensional Badola map implemented in an embedded system based on a 32-bit microcontroller to provide confidentiality of biomedical signals such as the electrocardiogram for applications in low-cost secure embedded systems in telemedicine for monitoring or diagnosis. The electrocardiogram (ECG) signal is acquired in real-time through a medical ECG module, which allows us to measure the ECG signal directly of the patient in real-time. In addition, we propose to use a simple non-linear function over the ECG signal to increase considerably the sensitivity at plain biosignal in the encryption process. In experimental results, several security analyzes are presented such as key space, secret key sensitivity, clear signal sensitivity, histograms, correlation coefficient and encryption time. The results show that the proposed chaotic encryption algorithm is resistant against common attacks and it can be used in secure low-cost embedded systems for telemedicine applications.

An Improved VME Technique via Heap Based Optimization Algorithm and AWIT Method for PLI and MA Noise Elimination in ECG

Diagnosing cardiac conditions require careful examination of an electrocardiogram (ECG). However, a significant issue arises when capturing an ECG due to interference from various noises. Noises like power line interference (PLI) and muscle artifact (MA) change the morphology, making it difficult to interpret the original signal. Our research proposes an improved variational mode extraction (IVME) technique using a Heap-based optimization (HBO) algorithm and an automatic wavelet interval-dependent thresholding (AWIT) method to eliminate such noises. First, HBO uses the envelope entropy spectrum (EES) as the objective function to find the best fitness value for optimizing the VME parameter, known as penalty factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> . Then, we extract a specific mode using the optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> value in VME to accurately remove PLI from the signal. Finally, the AWIT method automatically computes the intervals and their respective threshold values to remove excessive MA noise from the PLI-filtered ECG signal. We evaluate the efficiency of ten random real-time ECG signals from the MIT-BIH arrhythmia database. The result analysis proves that our algorithm can accurately extract the mode containing PLI and eradicate MA from the noisy ECG signal. It also shows improvement in signal parameters like signal-to-noise ratio (SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathrm {improvement}}$ </tex-math></inline-formula> ), mean square error (MSE), and correlation coefficients (CC) with 36.7968 dB, 0.00030901, and 99.7278%, respectively.

Graphene nano-platelets polyvinyl alcohol nanocomposite electrode for real time ECG signal acquisition

This paper highlights the development of graphene nanoplatelets polyvinyl alcohol(GNP@PVA) nanocomposite electrode to acquire real time Electrocardiogram(ECG) signal. In order to create GNP@PVA nanocomposite, solution casting was used. By drop casting the GNP@PVA nanocomposite onto a commercial silver/silver chloride (Ag/AgCl) electrode with different GNP concentrations, namely 0, 0.5, 0.75, and 1 wt/wt% are created. The 3-lead ECG system is designed using Arduino microcontroller, GNP@PVA electrode and AD8232 sensor. To investigate the physical, spectral, and microcrystalline characteristics of the prepared nanocomposites, the prepared GNP@PVA electrode is subjected to various analyses, including scanning electron microscope (SEM) analysis, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy. The results demonstrated that ECG signals acquired by GNP@PVA (1%) has increased R peak value along with high sensitivity. To validate the performance and efficiency of designed GNP@PVA electrode, the acquired real time ECG signal is compared with commercial Ag/AgCl electrode. In contrast to commercial Ag/AgCl electrode, the designed GNP@PVA dry electrode has high signal to noise ratio of 21 due to high conductivity.

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal.

In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.

Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and stationary wavelet transform

The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.

Self-adhesive, stretchable, and dry silver nanorods embedded polydimethylsiloxane biopotential electrodes for electrocardiography

Biopotential signals are extremely useful in assessing organ function and diagnosing diseases. This study proposes a highly flexible, conductive, antibacterial, and self-adhesive silver nanorods (AgNRs) embedded polydimethylsiloxane (PDMS) dry electrode for long-term electrocardiogram (ECG) monitoring with portable instrumentation. We use a unique glancing angle deposition method to fabricate AgNRs and embedded them in a biocompatible PDMS matrix. These electrodes do not cause skin irritation even after several hours of use and work efficiently without skin preparation. These AgNRs-PDMS electrodes have an electrical resistivity of 10−7 Ω m and a skin contact impedance of 93.9 ± 0.7 kΩ to 6.2 ± 3.7 kΩ for frequencies ranging from 40 Hz to 1 kHz, which is around 18% less than most conventional Ag/AgCl wet electrodes. The fabricated electrodes have a signal-to-noise ratio of 12 dB, comparable to that of a conventional Ag/AgCl electrode. A signal acquisition circuit is designed to detect ECG signals combined with proposed dry electrodes and a wireless monitoring device. Finally, real-time ECG signals are displayed on a mobile phone via an Android application. These AgNRs-PDMS dry electrodes, in combination with the portable wireless device, may be used in future clinical studies that require real-time and long-term ECG monitoring.

Internet of things based real-time vital sign monitoring system using mobile application

The development of telehealth technology in monitoring systems has been widely used to support applications in the health sector. The aim is to provide easy access for the people. One of the implications is a real-time monitoring system based on the Internet of Things (IoT) platform. Some health vital signs that focus on observation are electrocardiogram (ECG) signal, oxygen saturation, blood pressure, and heart rate, providing heart health information. In this study, an integrated system has been implemented, namely a vital sign distributed monitoring system through the internet network. The implemented system was able to acquire vital signs then send data to the internet cloud to be stored and processed further for real-time monitoring needs by interested parties. An Android-based application called iHealthVitalSign monitor can send, process, and represent data in numerical and graphical forms. The average delay for each packet delivery was 154.73 ms and complied with the ITU-T recommendations for real-time data transfer. Heart rate (HR)detection algorithms have been evaluated on real-time ECG signals. From the test results for more than 2100 beats, the average detection accuracy is 98.78%.With this proposed application, it is hoped that it can increase the penetration of telehealth services.

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this article. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined.

Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.

The Real-time Electrocardiogram Signal Monitoring System in Wireless Sensor Network

The Cardiovascular disease (CVD) is the most of death in the world. Electrocardiogram (ECG) is the graph that shows heart electrical activities. The physician record and detect the abnormal Electrocardiogram (ECG) signal by the Holter monitor that patient need to carry on the device for record ECG signal in 24 hours. Pan-Tomkins algorithm was appropriate for Real-time ECG signal recognition because high accuracy and rapidly analysis. This research propose the Real-time ECG Signal monitoring system for detect the abnormal ECG signal by using Pan-Tomkins algorithm with Wireless Sensor Network. The system separated into 2 part; sender module and receiver module. Experimental the system by using the ECG signal data from MIT-BIH database. Selected 20 samples of abnormal ECG signal then experimental at 10 and 20 meters sender module-receiver module distance, calculate R-R interval and R amplitude threshold The results show that the Real-time ECG signal monitoring system detect 17 abnormal ECG signal, the accuracy is 85%. This systems efficient for detect the abnormal of ECG signal in real-time.

Adaptive method with noise- and signal-dependent switching of filters for suppression of non-stationary noise in an electrocardiogram signal in real time

А new method for suppressing non-stationary noise in an electrocardiogram (ECG) in real time with a noise- and signal-dependent switching of filter with the most suitable processing of local signal segment is proposed. Adaptive algorithms are designed on the basis of this method. Statistical estimates of efficiency are obtained using such criteria as mean-square error, maximum absolute deviation, and signal-to-noise ratio for a model of ECG signal sampled at 1 kHz under conditions of different levels of additive Gaussian noise. It is shown that, with a very low noise level, the proposed algorithms do not distort the QRS-complex, and with a middle and high noise level, they provide a high degree of its suppression. In comparison with the modern dynamic algorithm of filtering of electromyographic (EMG) noise in an ECG, the proposed adaptive filters have an advantage in efficiency and smaller processing delay. Good quality of suppression of different levels of EMG noise in the ECG signal is demonstrated.

Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector.

BackgroundIn parallel to the introduction of mobile communication devices with high computational power and internet connectivity, high-quality and low-cost health sensors have also become available. However, although the technology does exist, no clinical mobile system has been developed to monitor the R peaks from electrocardiogram recordings in real time with low false positive and low false negative detection. Implementation of a robust electrocardiogram R peak detector for various arrhythmogenic events has been hampered by the lack of an efficient design that will conserve battery power without reducing algorithm complexity or ease of implementation.ObjectiveOur goals in this paper are (1) to evaluate the suitability of the MATLAB Mobile platform for mHealth apps and whether it can run on any phone system, and (2) to embed in the MATLAB Mobile platform a real-time electrocardiogram R peak detector with low false positive and low false negative detection in the presence of the most frequent arrhythmia, atrial fibrillation.MethodsWe implemented an innovative R peak detection algorithm that deals with motion artifacts, electrical drift, breathing oscillations, electrical spikes, and environmental noise by low-pass filtering. It also fixes the signal polarity and deals with premature beats by heuristic filtering. The algorithm was trained on the annotated non–atrial fibrillation MIT-BIH Arrhythmia Database and tested on the atrial fibrillation MIT-BIH Arrhythmia Database. Finally, the algorithm was implemented on mobile phones connected to a mobile electrocardiogram device using the MATLAB Mobile platform.ResultsOur algorithm precisely detected the R peaks with a sensitivity of 99.7% and positive prediction of 99.4%. These results are superior to some state-of-the-art algorithms. The algorithm performs similarly on atrial fibrillation and non–atrial fibrillation patient data. Using MATLAB Mobile, we ran our algorithm in less than an hour on both the iOS and Android system. Our app can accurately analyze 1 minute of real-time electrocardiogram signals in less than 1 second on a mobile phone.ConclusionsAccurate real-time identification of heart rate on a beat-to-beat basis in the presence of noise and atrial fibrillation events using a mobile phone is feasible.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).