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Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.

Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.

From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.

The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

The purpose of this paper is the development and study of capacitive coupling electrodes with the ability to monitor the quality of the skin–electrode contact in the process of electrocardiogram (ECG) diagnostics. The study’s scope embraces experimental identification of distortions contributed into the recorded ECG signal at various degrees of disturbance of the skin–electrode contact.

A capacitive coupling electrode is designed and manufactured. A large number of experiments was carried out to record ECG signals with different quality of the skin–electrode contact. Using spectral analysis, the characteristic distortions of the ECG signals in the event of contact disturbance are revealed.

It was found that the violation of the skin–electrode contact leads to significant deterioration in the recorded signal. In this case, the most severe distortions appear with various violations of the skin–electrode contact of two sensors in one lead. It has been experimentally shown that the developed sensor allows monitoring the quality of the contact, and therefore, improvement of the quality of signal registration, enabled by the use of bespoke processing algorithms.

These sensors will be used in personalized medicine devices and tele-ECG devices.

In this work, authors studied the effect of the skin–electrode contact of a capacitive electrode with the body on the quality of the recorded ECG signal. Based on the studies, the necessity of monitoring contact was shown to improve the quality of diagnostics provided by personalized medicine devices; the capacitive sensor with contact feedback was developed.

The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN).

The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time.

Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO₂ and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC).

The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future.

First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO₂, temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper.

The purpose of this paper is to design a low-power human physiological parameters monitoring system which can monitor six vital parameters simultaneously based on wearable body sensor network.

This paper presents a low-power multiple physiological parameters monitoring system (MPMS) which comprises four subsystems. These are: electrocardiogram (ECG)/respiration (RESP) parameters monitoring subsystem with embedded algorithms; blood oxygen (SpO₂)/pulse rate (PR)/body temperature (BT)/blood pressure (BP) parameters monitoring subsystem with embedded algorithms; main control subsystem which is in charge of system-level power management, communication and interaction design; and upper computer software subsystem which manipulates system function and analyzes data.

Results have successfully demonstrated monitoring human ECG, RESP, PR, SpO₂, BP and BT simultaneously using the MPMS device. In addition, the power reduction technique developed in this work at the physical/hardware level is effective. Reliability of algorithms developed for monitoring these parameters is assessed by Fluke Prosim8 Vital Signs Simulators (produced by Fluke Corp. USA).

The MPMS device provides long-term health monitoring without interference from normal personal activities, which potentially allows applications in real-time daily healthcare monitoring, chronic diseases monitoring, elderly monitoring, human emotions recognization and so on.

First, a power reduction technique at the physical/hardware level is designed to realize low power consumption. Second, the proposed MPMS device enables simultaneously monitoring six key parameters. Third, unlike most monitoring systems in bulk size, the proposed system is much smaller (118 × 58 × 18.5 mm3, 140 g total weight). In addition, a comfortable smart shirt is fabricated to accommodate the portable device, offering reliable measurements.

The purpose of the current study is to explore the potential possibility of acceleration in recognition, remedial process of heart disease and continuous electrocardiogram (ECG) signal acquisition. The textile-based ECG electrode is prepared by inkjet printing of activator followed by electroless plating of nickel (Ni) particle.

The electrical resistance shows a range of around 0.1 Ω/sq, which sounds quite proper for ECG signal acquisition, as the potential difference according to heart activity on skin surface is in milivolt range. Surface modifications of Ni–phosphorus (P)-plated polyester fiber were studied by scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffractionmethods. The quality of the acquired signal from printed square-shaped sensors in two sizes with areas of 9 and 16 cm2 compared with the standard Ag/Agcl electrode using commercial ECG with the patient in the sitting position.

Comparison of these data led to the consideration of small fabric sensor for better performance and the least disturbance regarding homogeneity and attenuation in electric field scattering. Using these types of sensors in textile surface because of flexibility will provide more freedom of action to the user. Wearable ECG can be applied to solve the problems of the aging population, increasing demand for health services and lack of medical expert.

In the present research, a convenient, inexpensive and reproducible method for the patterning of Ni features on commercial polyester fabric was investigated. Printed designs with high electrical conductivity can be used as a cardiac receiving signals’ sensor.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

Biometric scans using fingerprints are widely used for security purposes. Eventually, for authentication purposes, fingerprint scans are not very reliable because they can be faked by obtaining a sample of the fingerprint of the person. There are a few spoof detection techniques available to reduce the incidence of spoofing of the biometric system. Among them, the most commonly used is the binary classification technique that detects real or fake fingerprints based on the fingerprint samples provided during training. However, this technique fails when it is provided with samples formed using other spoofing techniques that are different from the spoofing techniques covered in the training samples. This paper aims to improve the liveness detection accuracy by fusing electrocardiogram (ECG) and fingerprint.

In this paper, to avoid this limitation, an efficient liveness detection algorithm is developed using the fusion of ECG signals captured from the fingertips and fingerprint data in Internet of Things (IoT) environment. The ECG signal will ensure the detection of real fingerprint samples from fake ones.

Single model fingerprint methods have some disadvantages, such as noisy data and position of the fingerprint. To overcome this, fusion of both ECG and fingerprint is done so that the combined data improves the detection accuracy.

System security is improved in this approach, and the fingerprint recognition rate is also improved. IoT-based approach is used in this work to reduce the computation burden of data processing systems.

The purpose of this paper is to provide appropriate methods for reducing the abnormalities on the extracted fetal heart signal from the maternal electrocardiogram (ECG).

In this regard, the extracted signal of the fetal heart from the mother, improved using an active noise cancelation (ANC) system. It uses commonly adaptive algorithms of normalized least mean squares (NLMS). In the present paper, fetal extraction and denoising methodology are proposed. This methodology uses a combination of the NLMS algorithm with Savitzky–Golay (S-G) filter.

The obtained results show that a combination of NLMS algorithm with filter coefficient of 15 and µ = 0.02 and S-G filter has a better qSNR (qSNR = 3.6727) and good performance for fetal ECG extraction in comparison with the other works for average fmSNR in the range of −30 to −15 dB. Also, with considering the SNR value of −24.7 dB before filtering and SNR = 3.1861 dB after filtering; the SNR improvement of 27.8861 dB has been obtained.

A new method in the extract and noise reduction of fetal ECG from maternal ECG by the combination of NLMS algorithm and S-G filter is proposed.

Recent developments in wearable technologies have paved the way for continuous monitoring of the electrocardiogram (ECG) signal, without the need for any laboratory settings. A number of wearable sensors ranging from wet electrode sensors to dry sensors, textile-based sensors, knitted integrated sensors (KIS) and planar fashionable circuit boards are used in ECG measurement. The purpose of this study is to carry out a comparative study of the different sensors used for ECG measurements. The current challenges faced in developing wearable ECG sensors are also reviewed.

This study carries out a comparative analysis of different wearable ECG sensors on the basis of four important aspects: materials and methods used to develop the sensors, working principle, implementation and performance. Each of the aspects has been reviewed with regard to the main types of wearable ECG sensors available.

A comparative study of the sensors helps understand the differences in their operating principles. While some sensors may have a higher efficiency, the others might ensure more user comfort. It is important to strike the right balance between the various aspects influencing the sensor performance.

Wearable ECG sensors have revolutionized the world of ambulatory ECG monitoring and helped in the treatment of many cardiovascular diseases. A comparative study of the available technologies will help both doctors and researchers gain an understanding of the shortcomings in the existing systems.