Signal Quality Research Articles

Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Immune challenge reduces begging effort and modifies begging call structure in spotless starling nestlings

Symptoms of illness offer insights into an organism’s condition, altering social signals that affect others’ behavior. Nestling birds employ begging signals to solicit parental care, but the extent to which begging reflects nestling health beyond hunger remains controversial. We investigated how experimentally induced changes in health affect begging signals in spotless starling (Sturnus unicolor) nestlings. To alter health status, we challenged nestlings by injecting lipopolysaccharide (LPS) -an antigen that elicits an immune response- or a control substance (PBS) and observed begging behavior under controlled food deprivation conditions. LPS-treated nestlings exhibited delayed gaping, reduced stretching, and less begging time compared to control nestlings. Moreover, LPS-treated nestlings produced calls with higher spectral entropy, particularly when deprived of food for longer. Our findings indicate that begging signals are sensitive to induced sickness. However, addressing mechanistic issues is crucial for effectively testing whether begging reflects nestling condition as a signal of quality in future experimental setups.

WITHDRAWN: A Technique for Enhancing Respiration Sensing in Interference Scenarios with Wi-Fi Signal

Wi-Fi-based non-contact respiratory monitoring technology plays a significant role in smart elderly care by eliminating the need for users to wear additional hardware devices. Although respiration sensing has shown impressive accuracy in ideal scenarios, achieving precise respiration monitoring in interfering scenarios, such as when other users are engaged in activities, remains challenging. This difficulty arises primarily from the weak nature of the respiratory signal, which is susceptible to interference from activities. To address this issue, this paper introduces the RespEnh system, a respiratory monitoring system that utilizes human location information to mitigate interference. Firstly, we enhance the quality of respiratory signal by leveraging the frequency diversity of Wi-Fi signal, effectively visualizing respiratory patterns even amidst interference. Secondly, we apply short time window shift to remove walking noise in the time domain while preserving respiratory signal characteristics. Finally, we integrate multi-antenna signals to enhance spatial domain performance. Experimental results demonstrate that our system achieves a relative anti-interference performance of 2.4m, even when breathing as far away as 6.4m. It effectively monitors person's respiration, with a detection rate of 80% within an error range of 1bpm.

Evaluating Neural Network Models For Predicting Dynamic Signature Signals

Abstract A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also contain dynamic information. This information is acquired using a digital input device, such as a graphic tablet or a digital screen and stylus. Information about the dynamics of the signing process is stored in the form of signals that change over time, including pen velocity, pressure, and more. These dynamics are characteristic of an individual and are difficult for a human to forge. However, it is an interesting research issue whether the values of signals describing a dynamic signature can be predicted using artificial intelligence methods. Predicting the dynamics of the signals describing a signature would benefit various scientific problems, including improving the quality of reference signals by detecting anomalies, creating signature templates better suited to individuals, and more effectively detecting potential forgeries by identity verification systems. In this paper, we propose a method for predicting dynamic signature signals using an artificial neural network. The method was evaluated using samples collected in the DeepSignDB database, distributed by BiDA Lab.

Preface

The 4th International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2024) is an annual conference focusing on research areas including materials science and engineering, chemical science and technology, and environmental engineering. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advance and development in materials, chemistry, and environmental engineering. This volume contains the papers of the 4th International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2024). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format. Cooperating with prestigious universities, CONF-MCEE 2024 organized four workshops in London, Tampa, Eskişehir, and Wuhan. Dr. Seyed Ghaffar chaired the workshop “The Influence of Nano-Additives in Low-Carbon 3D Printable Cementitious Materials”, which was held at the University of Birmingham. Dr. Ioannis Spanopoulos chaired the workshop “Perovskite Solar Cells Challenges and Promises” at University of South Florida. And Dr. Ömer Burak İSTANBULLU chaired the workshop “Preserving Clarity: Enhancing Image and Signal Quality for Computer-Aided Diagnosis and Treatment in Biomedical Engineering” at Eskişehir Osmangazi University. Besides these workshops, CONF-MCEE 2024 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Dr. Ioannis Spanopoulos from University of South Florida and Dr. Ömer Burak İSTANBULLU from Eskişehir Osmangazi University. They have given keynote speeches on related topics of materials, chemistry, and environmental engineering. On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-MCEE 2024, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-MCEE 2024. List of Committee Members are available in this Pdf.

Signal to Noise Ratio Improvement Using Stacking Method on Shallow Refraction Seismic Data

Abstract The improvement of signal-to-noise ratio in refraction seismic survey data is intriguing, especially when a long source-sensor distance and low energy vibration source is in use. In this work, we analyzed the effectiveness of the stacking procedure to extract geophone response signals buried under the noise level. The straight forward strategy to improve the signal quality is by doing multiple acquisition on every offset location and performing stacking procedure on the recorded data. For such activities, the sensor offset location is classified as near-offset (4-26m), middle-offset (28-50m) and far-offset (52-72m). To increase the usefulness of the proposed method, we developed the algorithm using an opensource programming language, and presented it in a step-by-step manner. We presented the procedure accompanied by the pre- and post- processing algorithm, consisting of seismic signal filtering and cross-correlation procedure in order to allow a precise determination of the first break time of the propagated seismic wave. The results give an overview on how to develop a seismic signal processing routine, as well as the possibility to improve the range of the refraction seismic instrumentation systems. The stacking procedure has a high level of significance for the middle offset. For the near offset the stacking procedure is not necessary, whereas for the far offset the large number of data is more important.

Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models

Soil heavy metal chromium pollution poses significant threats to human health and ecosystems, necessitating accurate quantitative prediction methods for effective monitoring and management. This study aims to develop robust predictive models for soil chromium content in farmland soils of Mojiang Hani Autonomous County, Pu’er City, Yunnan Province, China. These models utilize ensemble learning techniques based on visible and near-infrared spectroscopy. Operations before model building involved partitioning datasets with the Kennard-Stone algorithm to ensure representative training and testing sets. Visible and near-infrared spectroscopy-data preprocessing was performed using Savitzky-Golay smoothing and first-order derivative transformations to enhance signal quality. Bands selection was achieved through the Successive Projections Algorithm (SPA), effectively reducing data dimensionality and collinearity. Six ensemble learning models were constructed and assessed for their predictive performance: Bagging-DTR, Random Forest (RF), Adaboost-DTR, XGBoost-DTR, Stacking-1, and Stacking-2. These models utilized Decision Trees (DTR) and Linear Regression (LR) as base learners. Results demonstrated that ensemble models significantly outperformed individual base learners. Notably, the Stacking-2 model achieved the highest accuracy with an R^2 of 0.954, RMSE of 125.967 mg/kg, and RPD of 4.667. To validate the model’s practical applicability, spatial interpolation of soil Cr content was conducted using the Kriging method based on Stacking-2 model predictions. The spatial distribution maps of measured and predicted values exhibited high congruence, underscoring the model’s effectiveness in accurately mapping Cr distribution across the study area. This study underscores the efficacy of integrating ensemble learning with visible and near-infrared spectroscopy-data preprocessing and SPA for precise soil heavy metal prediction. The findings offer valuable insights and a scientific basis for enhanced soil quality monitoring, environmental risk assessment, and informed agricultural land management and pollution control.

Photonic stepped-frequency radar with 150-m unambiguous detection and centimeter range resolution.

Photonic stepped-frequency radars based on optical frequency-shifting modulation have shown attractive properties such as wide bandwidth, centimeter range resolution, inherent frequency-time linearity with low spectrum spurs, and reduced system complexity. However, existing approaches typically exhibit meter- or centimeter-level radar range ambiguity, inversely proportional to the frequency step, due to the large frequency shift determined by acousto-optic or electro-optic (EO) modulators. Here, we overcome this limitation by injecting a narrowband, stepped-frequency signal into an optical frequency-shifting fiber cavity to achieve, for the first time, to our knowledge, a broadband photonic stepped-frequency radar with 150-m unambiguous detection and centimeter range resolution, surpassing the reported photonic- and electronic-based counterparts. The demonstrated approach effectively resolves the trade-off between ambiguity range and shifting frequency while maintaining the signal quality and bandwidth, bringing its practicality into reach for outdoor applications.

High-Porosity Sieve-Type Neural Electrodes for Motor Function Recovery and Nerve Signal Acquisition.

In this study, the effects of electrode porosity on nerve regeneration and functional recovery after sciatic nerve transection in rats was investigated. A sieve-type neural electrode with 70% porosity was designed and compared with an electrode with 30% porosity. Electrodes were fabricated from photosensitive polyimide and implanted into the transected sciatic nerves. Motor function recovery was evaluated using the Sciatic Function Index. The number of active channels and their signal quality were recorded and analyzed to assess the sensory neural signal acquisition. Electrical impedance spectroscopy was used to evaluate the electrode performance. The group implanted with the 70% porosity electrode demonstrated significantly enhanced nerve regeneration and motor function recovery, approaching control group levels by the fifth week. In contrast, the group with the 30% porosity electrode exhibited limited improvement. Immunohistochemical analysis confirmed extensive nerve fiber growth within the 70% porous structure. Moreover, the 70% porosity electrode consistently acquired neural signals from more channels compared to the 30% porosity electrode, demonstrating its superior performance in sensory signal detection. These findings emphasize the importance of optimizing electrode porosity in the development of advanced neural interfaces, with the potential to enhance clinical outcomes in peripheral nerve repair and neuroprosthetic applications.

Challenges and Solutions in High-Speed SerDes Data Path Design

Modern high-performance communication systems require high-speed Serializer/Deserializer (SerDes) data path design to meet the growing demand for data throughput and reliability due to technological advancements and data-intensive applications. This research article examines the complex problems of designing high-speed SerDes data routes and suggests novel solutions. Data fidelity across high-speed channels requires signal integrity, the first key difficulty. Attenuation, crosstalk, and EMI may degrade SerDes signal performance. Advanced methods like equalization, impedance matching, and effective shielding mitigate these impacts. Equalization, including feedforward and feedback methods, reduces channel losses and distortions, enhancing signal quality. Power consumption is another major issue as data rates and system complexity rise. Power-hungry high-speed SerDes circuits cause thermal management and energy efficiency difficulties. Adaptive power management, low-power design, and new materials with higher thermal performance and lower power dissipation are discussed in the study. Design complexity is another issue in high-speed SerDes data route design. High-precision timing and synchronization and many component integration in a small area need advanced design tools and methods. The study emphasizes simulation and modeling for complexity management and design for testability (DFT) for full verification and debugging.

Design of a Frequency Controller System for UHF Radio Wave Receiver Device

Frequency is fundamental in data communication for transmitter and receiver stations. The most common problem found in the data communication is the signal quality measurement. A LoRa module, as one of the wireless devices with UHF (Ultra High Radio) frequencies, has extended range frequencies and signal strength parameters (RSSI, Received Signal Strength Indicator). RSSI parameter on signal bandwidth and the distance between stations. However, there is no supporting research in terms of the RSSI quality and short-distance variations. In line with this, this study aims to design a frequency control system of a LoRa module and to investigate the effect of short-distance and frequency differences on RSSI. The system consists of a transmitter, a LoRa SX1278 module, and push buttons as frequency control buttons. The system's performance was tested by receiving data over various distances from 0 to 1.25 m. The results show that the system works efficiently in controlling the frequency (ranging from 433 – 525 MHz) with good flexibility and accuracy. The system maintained an RSSI level > -120 dBm using a constant power supply and varied distances. The highest RSSI level is found at the shortest distance (0.2 m) with a higher frequency (525 MHz). There is a significant correlation between distance, frequency variations, and RSSI levels (R2 > 0.75). It can be concluded that the LoRa SX1278 module can be used as a short-distance-based data communication with moderate quality (average RSSI levels are about 80 - 120 dBm). Higher frequencies provide better RSSI levels due to increased transmission energy.

Evaluating Signal Quality and System Performance in NB-IoT Communications

The expansion of the Internet of Things (IoT) has created a need for reliable and fault-tolerant communication networks. However, ensuring consistent signal quality and power efficiency has proven to be challenging. This study evaluated the performance of Narrowband IoT (NB-IoT) communications using the SIM7020 module connected to a Raspberry Pi 4 Model B, focusing on signal quality across indoor, outdoor, urban and rural areas. Supervised machine learning for indoor localisation based on Received Signal Strength Indicator (RSSI) has been introduced, for example, to enhance NB-IoT performance. However, this and other approaches have encountered difficulties in mobile and obstructed environments, including signal attenuation, connectivity variability and increased power consumption. The objective of this study was to analyse NB-IoT signal strength and power consumption, providing guidance for deploying real-time communication IoT applications. Empirical data was analysed to understand the RSSI and Cellular Signal Quality (CSQ) in different locations. Signal quality in urban and outdoor environments was prone to fluctuations due to mobility and interference, whereas rural areas had weaker but more consistent signals. Indoor environments suffered from significant signal attenuation. The results emphasise the importance of improved handover mechanisms and adaptive deployment strategies to ensure reliable connectivity across various IoT applications.

Conductive Hydrogel Tapes for Tripolar EEG: A Promising Solution to Paste-Related Challenges.

Electroencephalography (EEG) remains pivotal in neuroscience for its non-invasive exploration of brain activity, yet traditional electrodes are plagued with artifacts and the application of conductive paste poses practical challenges. Tripolar concentric ring electrode (TCRE) sensors used for EEG (tEEG) attenuate artifacts automatically, improving the signal quality. Hydrogel tapes offer a promising alternative to conductive paste, providing mess-free application and reliable electrode-skin contact in locations without hair. Since the electrodes of the TCRE sensors are only 1.0 mm apart, the impedance of the skin-to-electrode impedance-matching medium is critical. This study evaluates four hydrogel tapes' efficacies in EEG electrode application, comparing impedance and alpha wave characteristics. Healthy adult participants underwent tEEG recordings using different tapes. The results highlight varying impedances and successful alpha wave detection despite increased tape-induced impedance. MATLAB's EEGLab facilitated signal processing. This study underscores hydrogel tapes' potential as a convenient and effective alternative to traditional paste, enriching tEEG research methodologies. Two of the conductive hydrogel tapes had significantly higher alpha wave power than the other tapes, but were never significantly lower.

Quantum-dot spin-VCSELs subject to optical injection and feedback for flexible photonic millimeter wave generation

A novel microwave and millimeter wave signal generation system is proposed and numerically demonstrated using an optically pumped quantum dot (QD) spin-polarized vertical-cavity surface-emitting laser (spin-VCSEL) with optical injection. Different from previous optical injection systems, the signal frequency in our proposed system is dominated by both the birefringence-splitting and injection detuning frequencies. By flexibly tailoring the frequency detuning and birefringence rate, the frequency ranges from a few gigahertz to nearly 150 GHz can be obtained. To evaluate the signal quality, the dynamical behaviors of the proposed system and the characteristics of the generated millimeter wave signal are evaluated including the center frequency, power, linewidth, and phase variance. We further adopt the optical feedback structure to improve the microwave and millimeter wave signal performance. Results show that the signal linewidth and phase variance can be optimized by three to four orders of magnitude. Compared to the case of a solitary QD spin-VCSEL, this work shows more stable performance, particularly in the frequency range above 90 GHz. This scheme offers an approach for high-purity microwave and millimeter sources in wireless communication and radar systems.

The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments.

Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey

Recently, extensive research has been conducted to explore the utilization of machine learning (ML) algorithms in various direct-detected and (self)-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, ML demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms such as feed-forward/decision-feedback equalizers (FFEs/DFEs) and Volterra-based nonlinear equalizers, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). Time-series ML models offer distinct advantages over frequency-domain models in specific contexts. They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this survey, we outline the application of ML techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. We introduce a novel taxonomy for time-series methods employed in ML signal processing, providing a structured classification framework. Our taxonomy categorizes current time-series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of ML approaches in short-reach optical communication systems by addressing complexity concerns.

Optimising signal quality for corrosion detection using the innovative Mag_FSRCNN model to minimise hall sensor array in eddy current testing

ABSTRACT The security of aviation hinges significantly on the strength of aircraft structures, with potential risks arising from issues like corrosion impacting the framework. Eddy current testing (ECT) sensors, particularly those utilising array technology, present a promising solution to address this concern. However, challenges persist in improving the clarity of corrosion signals while optimising associated expenses. In response to this, our study introduces the Mag_FSRCNN model, a novel framework that integrates a Fast Super-Resolution Convolutional Neural Network (FSRCNN) module network with a part of Generator module of Generative Adversarial Network to enhance magnetic images obtained by the ECT sensor array. Mag_FSRCNN surpasses traditional methods, showcasing high-resolution capabilities in enhancing ECT signal images. The model demonstrates excellence when evaluated using metrics such as Structural Similarity Index, Root Mean Square Error, and Peak Signal-to-Noise Ratio. These results significantly contribute to the ongoing progress of ECT sensors, facilitating improved resolution and cost-effective application of Hall sensor arrays in the current evolution of ECT sensor technology.

Influence of detector temperature on Doppler signal quality of laser Doppler velocimeter

Enhancing the quality of Doppler signal holds paramount importance for the utilization of Laser Doppler Veolcimeter in velocity measurement of high-speed vehicles, underwater vehicles, and aerial vehicles. This paper discusses the variation of Doppler signal and noise with bias voltage at different temperatures, ultimately deriving the relationship between the signal-to-noise ratio of Doppler signal and detector temperature as well as bias voltage. And a linear correlation is established between detector temperature and optimal bias voltage. Furthermore, A bias control circuit for temperature compensation is designed for maximizing signal-to-noise ratio of Laser Doppler Velocimeter under varying temperatures.

Optimal signal quality index for remote photoplethysmogram sensing

Remote photoplethysmography (rPPG) enables non-invasive monitoring of circulatory signals using mobile devices, a crucial advancement in biosensing. Despite its potential, ensuring signal quality amidst noise and artifacts remains a significant challenge, particularly in healthcare applications. Addressing this, our study focuses on a singular signal quality index (SQI) for rPPG, aimed at simplifying high-quality video capture for heart rate detection and cardiac assessment. We introduce a practical threshold for this SQI, specifically the signal-to-noise ratio index (NSQI), optimized for straightforward implementation on portable devices for real-time video analysis. Employing (NSQI < 0.293) as our threshold, our methodology successfully identifies high-quality cardiac information in video frames, effectively mitigating the influence of noise and artifacts. Validated on publicly available datasets with advanced machine learning algorithms and leave-one-out cross-validation, our approach significantly reduces computational complexity. This innovation not only enhances efficiency in health monitoring applications but also offers a pragmatic solution for remote biosensing. Our findings constitute a notable advancement in rPPG signal quality assessment, marking a critical step forward in the development of remote cardiac monitoring technologies with extensive healthcare implications.