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A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

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 manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

This paper aims to describe and characterise the actions carried out by Italian organisations participating in the Economy for the Common Good (ECG) movement and to analyse these actions through the lens of decent work (DW), identifying patterns leading to a typology and conceptual propositions on the subject.

A documentary analysis was conducted on 14 reports describing the actions taken by Italian organisations that belong to the ECG movement. Qualitative content analysis was performed using QSR-NVivo12. The descriptive analysis of the codes was made, as well as a cluster analysis based on coding similarity.

A total of 1,497 actions were coded, and four clusters, grouping sets of the common good reports, were identified. Results suggest that Customers, Business Partners and Staff and Owners are the most addressed stakeholders, human dignity and environmental sustainability are the most addressed values and Fulfilling and Productive Work and Fundamental Principles and Values at Work are the most addressed DW dimensions. Additionally, all clusters are intensive in environmental concerns but have differentiated priorities. Cluster analysis suggests three drivers: recognition, core business closeness and social common good impact. A total of five conceptual propositions are being made useable by organisational leaders who intend to adhere to the ECG movement.

The main limitation is the low number of organisations participating in the ECG movement in Italy, which restricts the scope of the conclusions.

The results are helpful as inputs for designing interventions in organisations that intend to start or strengthen their involvement in the ECG movement.

Identifying DW aspects related to common good indicators and the four approaches to the ECG adhesion corresponding to the four clusters.

This paper briefly describes the technical aspects of ECG interpretation by computer, and thereafter discusses in detail the considerations involved in introducing the method into a large hospital. One of the most important of these is that of educating physicians to accept 3‐lead ECG interpretations. Technical problems are discussed together with the question of staffing, and it is emphasized that the introduction of automation does not normally lead to any staff redundancy. The various shortcomings of existing methods are described but the benefits accruing from automation are discussed. The conclusion drawn is that the technique will prove to be of value in the near future when regional centres are established to cope with the ever increasing demand for ECG interpretations.

The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications.

The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations.

The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation.

The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals.

The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style.

The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

The hardware, software and systems components of a Clinical Research ECG Station using an IBM‐1800 computer under the Multi‐Programming Executive System (MPX) is described. The twelve classic leads and the three Frank leads of the patient‐acquired ECG are sent in analog form over telephone lines via Marquette Electronics' carts and receiving interface, which modulate and demodulate the signal for computer acquisition, subsequent analog to digital conversion, and analysis. The Smith‐Mayo program (on the Frank leads) initially used, has been augmented by a Hahnemann‐developed, twelve‐lead analysis program to improve the interpretive portion of the program in the areas of LVH, Infarctious, Bundle Blocks, etc. A truly innovative portion of the system is the creation of a computer‐driven microfilm system which produces aperture cards. These contain all the graphical input on microfilm, plus graphs of the two‐dimensional vector curves and the appropriate patient identification information. Archive tapes of the digitized ECG are kept as a data repository for (1) the constant reworking of the analysis program, (2) the development of proper comparison programs, and (3) the research potential of using existing Probabilistic‐Statistical Differential Diagnosis methods to quest the nature of what symptomatic information is necessary to optimize the diagnostic process in the ECG area.

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.

This study aims to analyze the differences of electrocardiograph (ECG) characteristics for female drivers in calm and anxious states during driving.

The authors used various materials (e.g. visual materials, auditory materials and olfactory materials) to induce drivers’ mood states (calm and anxious), and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data. Physiological changes in ECG during the stimulus process were recorded using PSYLAB software. The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.

The results show significant differences in the characteristic parameters of female driver’s ECG signals, including (average heart rate), (atrioventricular interval), (percentage of NN intervals > 50ms), (R wave average peak), (Root mean square of successive), (Q wave average peak) and ( S wave average peak), in time domain, frequency domain and waveform in emotional states of calmness and anxiety.

Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving. It can be used for the development of personalized driver assistance system and driver warning system.

Only a few attempts have been made on the influence of human emotions on physiological signals in the transportation field. Hence, there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states. This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.