Electrocardiogram Signal Acquisition Research Articles

Robust conductive hydrogel advances self-powered intelligent sports monitoring and fair judging

Currently, combining multimodal perceptual ability with mechanical robustness poses a significant challenge for developing hydrogel-based flexible electronics. Herein, a robust conductive hydrogel was successfully synthesized by introducing cellulose nanocrystals (CNC) as nanofillers and lithium chloride (LiCl) as the conductive component into the copolymer network through a one-pot crosslinking procedure in a water-dimethyl sulfoxide binary solvent. Besides excellent stretchable, self-adhesive, anti-freezing, self-healing, and anti-swelling properties, tunable and reversible optical property was realized due to the polarity-induced microphase separation of the hydrophilic and hydrophobic group in the zwitterionic sulfobetaine chains, enabling cyclic information encryption and decryption. An all-weather wearable sensor array was thus developed for multi-modal sensing of various environmental stimuli, including mechanical, and thermal variations. Specifically, the intensity and spatial distribution of strain can be detected almost in real time within a broad strain range, thus realizing speech recognation, text-to-speech and human electrocardiogram signal acquisition and underwater non-contact sensing functions. Notably, the CPAMD-based triboelectric nanogenerator demonstrates impressive temperature tolerance (−40 °C and 60 °C), and remarkable material identification capability. A non-contact proximity sensing sensor and a foul detection system were thus developed by coupling the piezoresistive, triboelectric and capacitive sensing metrics to serve as an electronic referee for identifying foul in curling. This work provides a valuable reference toward combining excellent mechanical robustness with balanced electrical performance for tactile perception and intelligent sports monitoring.

Design of ECG Circuit and Patient Remote Monitoring System Using MQTT Protocol for ECG Signals

In the healthcare sector, leveraging the Internet of Things (IoT) brings significant advantages, especially in remote monitoring of patients' health using electrocardiogram (ECG) signals. This study employs IoT technology to enable remote ECG monitoring, utilizing the MQTT (Message Queuing Telemetry Transport) protocol to establish a web server. The dedicated measuring device integrates the ADAS1000-3 IC with ESP32 for real-time ECG signal acquisition. In this study, we utilize the Pan-Tompkins algorithm to extract QRS complexes from electrocardiographic signals, count heartbeats, and early detect cardiovascular abnormalities. This solution was tested on a sample dataset from the MIT-BIH (Massachusetts Institute of Technology - Beth Israel Deaconess Medical Center) database. Finally, the article proposes to design and develop a device using advanced IC technologies such as IC ADAS1000-3 combined with ESP32. This device will be designed to meet the requirements of real-time processing and data transmission via webserver.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

PACKET ANALYZER OF ONLINE ECG SIGNALS

This paper presents the implementation of online acquisition software for electrocardiogram (ECG) signals. This program is based on the Python language and it allows the adaptation of a commercial offline signal acquisition board into an online signal acquisition board. Here, the concept of online acquisition is applied when the signal can be analyzed in short time intervals, such as a few seconds. The developed system is divided into two parts: the acquisition module; and the graphical user interface (GUI). The acquisition module is responsible for recording and processing the raw signal, making it available to the graphics module. The developed structure allows the algorithm to be used in more complex applications, abstracting the acquisition logic. Furthermore, the acquisition module has acquisition settings, defined by the user when initializing the module. This work presents a simple example of an application using the module. In this case, the system stores the information from the acquisition module and constantly plots it on the screen. The program also integrates settings available by code, using command-line arguments. Therefore, the developed system presents itself as a powerful tool for online ECG signal acquisition, allowing the use of the board in human-machine interfaces.

On‐Skin Paintable Water‐Resistant Biohydrogel for Wearable Bioelectronics

AbstractTo achieve accurate monitoring of bioelectrical signals, it is essential to use customizable bioelectrodes that can self‐adapt to the skin's surface topography. Ion‐conducting hydrogel has received significant attention in this field due to its softness, adhesion, and skin‐like mechanical properties. However, these bioelectrodes currently suffer from degradation of adhesion, electrical conductivity, and skin‐compliance when exposed to aqueous environments. This significantly limits the application of bioelectrodes. Herein, a customizable biohydrogel that can be applied on skin or fabric by solvent volatilization for liquid ink‐gel film conversion is reported. The biohydrogel's distinct characteristic of transitioning between a liquid and hydrogel phase establishes superb conformal contact and dynamic compliance with the epidermis. This effectively eliminates motion artifacts and results in lower contact impedance and noise in both static and dynamic states when compared to existing bioelectrodes. The biohydrogel is applied to the cotton fabric to create electrocardiogram (ECG) monitoring garments. These garments enable the acquisition of ECG signals with high accuracy in aqueous environment for over 72 h. Besides, the biohydrogel‐based garments outperform the commercial gel electrodes by 83.5% in signal‐to‐noise ratio. Additionally, the cotton/biohydrogel electrode facilitates multi‐channel, high‐fidelity recording of ECG signals, enabling high‐performance capture and classification of ECG waveforms across multiple channels.

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.

Dry electrode geometry optimization for wearable ECG devices

Wearable electronic devices, particularly for health monitoring, have seen rapid advancements in recent times. Among the various biophysical parameters that are of interest in a wearable device, an electrocardiogram (ECG) is critical as it enables detection of cardiovascular-related ailments and assessment of overall cardiac health. In a wearable ECG device, the choice of electrode design and material plays a key role in the performance of the sensor. In this work, we have explored various dry electrode-based sensor design geometries to realize a compact, lightweight, portable, gel-free wearable ECG patch that would aid in point-of-care (PoC) diagnostics. Furthermore, we have studied the influence of the region of the body at which the measurements were made under different body positions across varying external stimuli. We have studied the influence of surface area, perimeter and resistance offered by the electrodes on the ECG signal acquisition, its effects on device performance and found the hexagonal labyrinth configuration to be the most suitable candidate. A prototype of a wearable ECG patch was made by combining this electrode configuration and interfacing with wireless communication capabilities, and the results were compared with a commercially available portable ECG monitor. Such a device could find potential application in remote healthcare and ambulatory care settings, and as a PoC and a preventive medical device.

ECG SIGNAL QUANTITATIVE ANALYSIS BASED ON EXTREMUM ENERGY DECOMPOSITION METHOD

Quantitative analysis of electrocardiogram (ECG) signals plays a pivotal role in objectively and quantitatively assessing cardiac electrical activity. This paper presents an innovative approach for quantitative ECG signal analysis utilizing extremum energy decomposition (EED). The methodology encompasses multiple steps: acquisition of unknown ECG signal under specific time and sampling conditions, denoising of acquired ECG signals, and subsequent decomposition of denoised ECG signals into a set of extremum modal function components alongside a residual. The n extremum modal function components obtained effectively represent different frequency bands. By evaluating these n extremum modal function components, the presence and severity of abnormalities within the ECG signal can be determined. The results showcased the effectiveness of the method in accurately identifying abnormal ECG signals, and the technique demonstrated robustness against noise interference, enhancing its practical utility in clinical and diagnostic settings. This research contributes to the field of ECG analysis by offering a quantitative toolset that enhances the objectivity and accuracy of abnormality assessment in cardiac electrical activity.

AS3-SAE: Automatic Sleep Stages Scoring Using Stacked Autoencoders

Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In recent years, automatic sleep monitoring using deep learning networks has attracted the attention of researchers.
 Materials and Methods: In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used for automatically classifying sleep stages. SAEs are a kind of neural network with encoder and decoder blocks. The function of these networks is similar to the human brain and is capable of automatically processing signals; also SAEs are robust to noise. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) on the performance of sleep stage classification, Sleep Heart Health Study (SHHS) and ISRUC standard databases have been used, which include night recordings of 30 and 10 healthy humans, respectively. 
 Results: The accuracy of classifying 2 to 6 classes by SHHS database are 0.995, 0.983, 0.9780, 0.9688, 0.961, and on ISRUC database accuracies are 0.996, 0.994, 0.9511, and 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc = 0.75, kappa = 0.69).
 Conclusion: In the review of the results, it is concluded that sleep stages classification based on EEG signal has better results, still acquisition of ECG signal and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals.

Washable and Flexible Screen-Printed Ag/AgCl Electrode on Textiles for ECG Monitoring.

Electrocardiogram (ECG) electrodes are important sensors for detecting heart disease whose performance determines the validity and accuracy of the collected original ECG signals. Due to the large drawbacks (e.g., allergy, shelf life) of traditional commercial gel electrodes, textile electrodes receive widespread attention for their excellent comfortability and breathability. This work demonstrated a dry electrode for ECG monitoring fabricated by screen printing silver/silver chloride (Ag/AgCl) conductive ink on ordinary polyester fabric. The results show that the screen-printed textile electrodes have good and stable electrical and electrochemical properties and excellent ECG signal acquisition performance. Furthermore, the resistance of the screen-printed textile electrode is maintained within 0.5 Ω/cm after 5000 bending cycles or 20 washing and drying cycles, exhibiting excellent flexibility and durability. This research provides favorable support for the design and preparation of flexible and wearable electrophysiological sensing platforms.

Chronological golden search optimization-based deep learning for classification of heartbeat using ECG signal

ABSTRACT Electrocardiogram (ECG) is the simplest test, used for checking the heart’s rhythm and measuring the heart’s electrical activity. This method is the most probably used technique for identifying heart diseases because of its non-invasive nature. The patient’s status is identified by handling the irregularities in the ECG signals. For computation in the existing system, they use signal processing methods which result in time consumption in real time. In existing system, it is hard to predict the detection of heartbeat early and accurately. In this work, the heartbeat is classified using ECG signal by proposed Chronological Golden Search Optimisation (CGSO)-based Deep Learning (DL). Here, the CGSO algorithm is developed by hybridising the Chronological concept with the Golden search optimisation (GSO) algorithm, which is utilised to train SqueezeNet. After the acquisition of ECG signals, pre-processing is done by a median filter. Then, the features are extracted, and this tends to the data augmentation process. Finally, the heartbeat is classified using SqueezeNet-enabled CGSO. The performance is analysed using ECG heartbeat categorisation data set. The proposed model obtained a specificity of 93.5%, sensitivity of 93.1% and precision of 89.8%. From the results, it is known that the proposed system offers more accurate results.

Wireless CardioS framework for continuous ECG acquisition

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as “CardioS” (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag–AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (S band/N band), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5–54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm2, as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring.

A programmable analog front-end with independent biasing technique for ECG signal acquisition

This paper presents a programmable low power and low noise analog front-end (AFE) for electrocardiogram (ECG) signal acquisition. A novel capacitor-coupled instrumentation amplifier (CCIA) features independent biasing technique and AC coupling to signal source is proposed. Independent biasing technique is capable of obtaining better noise performance and more stable static working point reducing the glitch of chopping. An AC coupling style helps suppress electrode DC offset and enhance input impedance with metal-oxide-metal capacitor array, which is symmetrical to the center to alleviate the mismatch of input capacitor enhancing system common mode rejection ratio (CMRR). The current steering technology (CST) and programmable feedback capacitors are utilized to achieve an area-efficient programmable low pass filter (PLPF) under PVT variations. Also, a charge buck after PLPF is introduced to strengthen the driving ability of AFE. To verify the proposed AFE structure, a prototype is designed in 0.13 μm standard CMOS process with a 1.2 V supply. Post-simulation results show that the input impedance reaches 1.4 GΩ under 50 Hz. The in-band white noise density and integrating noise are 48 nV/sqrt (Hz) and 0.6 μV, respectively. The obtained low pass corner of PLPF are 380 Hz, 451 Hz and 1 kHz. The CMRR and PSRR under montecarlo simulation are 93 dB and 77 dB, respectively. The power of whole AFE is 48 μW.

Optimized Tunable-Q Wavelet Transform-Based 2-D ECG Compression Technique Using DCT

Electrocardiogram (ECG) signals are one of the fundamental health indicators used in the continuous monitoring and diagnosis of severe heart diseases. The acquisition of long-term ECG signals generates large data that negatively affect the proficiency of the transmission channel and storage devices. In this regard, this work presents a novel compression algorithm by combining discrete cosine transform (DCT) and optimized tunable-Q wavelet transform (TQWT) for compression of 2D ECG signals. The proposed algorithm is applied to columns and rows of 2D ECG array, which improves the compression by increasing sparsity in the transform domain. Thereafter, transform coefficients are approximated using an optimized midtread quantizer. TQWT and quantizer’s parameters both are optimized using a recently developed COOT bird optimization algorithm, which simulates the behavior of COOT birds. The obtained quantized coefficients are encoded by adaptive Huffman encoding. The investigational work is tested on MIT-BIH arrhythmia database. The optimization performance of COOT algorithm is also validated by comparing it with some existing algorithms. Similarly, compression performance is also compared with the state-of-art compression techniques in terms of compression ratio, signal-to-noise ratio, percent root-mean-square difference, and quality score. The obtained average values of these parameters are 30.7826, 27.7073 dB, 4.5162 %, and 8.1349, respectively. Attained results strengthen the applicability of proposed method in telemedicine or mobile health-based monitoring systems and memory devices.

Design and Development of Cloud Based Real Time ECG Anomaly Detection

The functionality of the heart is assessed by analysing the electrocardiogram (ECG) signal. Numerous methods were developed for the accurate detection of heart diseases utilising the recorded ECG signals. However, individuals with serious heart diseases must be constantly observed, especially if there are few indicating bio-signal patterns. Real-time data collection and processing enables early detection of heart diseases and prevent complications. The AD8232 sensor is used in the project along with its interface with the Node MCU for real time acquisition of ECG signal. The intervals of the PQRST wave are thoroughly investigated utilizing a novel windowing approach based on Wavelet Transform. To increase the QoS in the healthcare system, the final findings are displayed on both the system and the cloud interface. In this work, we compared the ECG parameters with those from well-known smartwatches. The outcomes demonstrated a striking match between the ECG parameters acquired using our method and those obtained using smartwatches. This verifies the precision and dependability of our system in monitoring the ECG parameters from ECG signal. Our methodology has the potential to be a dependable and practical solution for heart rate monitoring in real-time and distant healthcare applications because our algorithm and smartwatches consistently report heart rate rates in the same range.

Motion Artifact Reduction in Electrocardiogram Signals Through a Redundant Denoising Independent Component Analysis Method for Wearable Health Care Monitoring Systems: Algorithm Development and Validation.

The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices. We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method's performance and to compare its performance to that of existing approaches. We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient's chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis. The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG. Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices' cost-effectiveness and contributes to their widespread application.

Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real-time ECG signal acquisition.

To investigate the feasibility of cardiac synchronized gating in stereotactic body radiation therapy (SBRT) of ventricular tachycardia (VT) using a real-time electrocardiogram (ECG) signal acquisition. Stability of beam characteristics during simulated ECG gating was examined by developing a microcontroller interface to a Varian Clinac iX linear accelerator allowing gating at frequencies and duty cycles relevant to cardiac rhythm. Delivery accuracy was evaluated by measuring dose linearity with an ionization chamber, and flatness and symmetry with a two-dimensional detector array, for different gating windows within typical human cardiac cycle periods. To establish a practical method of gating based on actual ECG signals, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the beam delivery. Real-time cardiac gated delivery measurements were performed for a single 10×10cm2 field and for a VT-SBRT plan using intensity-modulated radiation therapy (IMRT). Dose per monitor unit (MU) values were found to be linear within most gating windows investigated with maximum differences relative to non-gated delivery of <2% for gating windows ≥200ms and for >10MUs. Beam profiles for both gated and non-gated modes were also found to agree with maximum differences of 0.5% relative to central axis dose for all sets of beam-on/beam-off combinations. Comparison of dose distributions for intensity-modulated SBRT plans between non-gating and cardiac gating modes provided a gamma passing rate of 97.2% for a 2% 2mm tolerance. Beam output is stable with respect to linearity, flatness, and symmetry for gating windows within cardiac cycle periods. Agreement between dose distributions for VT-SBRT using IMRT in non-gated and cardiac cycle gated delivery modes shows that the proposed methodology is feasible. Technically, gating for delivery of SBRT for VT is possible with regard to beam stability.

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.

Dual phase dependent RLS filtering approach for baseline wander removal in ECG signal acquisition

Electrocardiogram (ECG) signals often get contaminated during physical activity, due to baseline disturbances. Removal of baseline interference also leads to variation in the timing of ECG waves. This paper proposes a dual phase dependent-recursive least square (DPD-RLS) adaptive filter that removes the baseline interference during ECG signal acquisition. This approach initially detects the upper envelope followed by the subtraction of the upper envelope with the original signal. The lower envelope is then detected from the resultant signal. Two phases are extracted from the upper envelope signal and the lower envelope signal that compensates for the phase change effects during the baseline wander removal process. A DPD-RLS adaptive filter structure is proposed that compensates for the phase changes near the P, Q, and T onset of the ECG signal. The evaluation was done using the clean ECG signals from the MIT-BIH arrhythmia dataset and baseline wander signals from MIT-BIH noise stress dataset signals using the metrics such as output signal to noise ratio (SNRo), correlation coefficient (γ), and percent root mean square difference (PRD) for the different input signal to noise ratios (SNRi). The proposed scheme provides an average output SNR, the correlation coefficient of 14.07dB and 0.980 respectively at SNRi=5dB when evaluated on different ECG records. Experimental results reveal that the proposed baseline wander removal scheme outperforms the traditional schemes.

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals.

We developed an infrared (IR)-based real-time online monitoring device (US Patent No: US 10,571,448 B2) to quantify heart electrocardiogram (ECG) signals to assess the water quality based on physiological changes in fish. The device is compact, allowing us to monitor cardiac function for an extended period (from 7 to 30 days depending on the rechargeable battery capacity) without function injury and disturbance of swimming activity. The electrode samples and the biopotential amplifier and microcontroller process the cardiac-electrical signals. An infrared transceiver transmits denoised electrocardiac signals to complete the signal transmission. The infrared receiver array and biomedical acquisition signal processing system send signals to the computer. The software in the computer processes the data in real time. We quantified ECG indexes (P-wave, Q-wave, R-wave, S-wave, T-wave, PR-interval, QRS-complex, and QT-interval) of carp precisely and incessantly under the different experimental setup (CuSO4 and deltamethrin). The ECG cue responses were chemical-specific based on CuSO4 and deltamethrin exposures. This study provides an additional technology for noninvasive water quality surveillance.