The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

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An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

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Automatic QRS complex detection using two-level convolutional neural network

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.

A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Cardiac-related diseases have been one major cause of death for an ever increasing number of patients over the last few decades throughout the world. In response, automatic classification of cardiac rhythms using Heart Rate Variability analysis as an effective diagnostic tool has recently emerged as an important field of research. Previous researches has proved that translating and transforming HRV data into numbers can introduce highly accurate assessments of rhythm disorders. However, to obtain reliable HRV interpretation, accurate QRS detection approaches must be utilized. This work, as motivated by the arguments just presented, reviews in detail the most recent and efficient techniques related to QRS feature extraction and HRV determination all classified and presented in a convenient fashion to facilitate coverage. The study also presents a state-of-the-art updated review on QRS detection and heart rate variability analyses that could serve as a handy future reference in this field of research based on more than 200 articles reviewed in this effort.

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QRS Detection and Heart Rate Variability Analysis: A Survey

Coronary angiography image segmentation based on PSPNet.

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

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Real time QRS complex detection using DFA and regular grammar

We propose in this work a method of electrocardiogram (ECG) signal pretreatment by the application of Discreet Wavelet Transform DWT by automatically determining the optimal order of decomposition. After the purification of the original signal, we describe an algorithm to detect R waves based on the Dyadic Wavelet Transform DyWT by applying a windowing process. This algorithm is validated on a sample of synthesis ECG signal with and without noise which we have proposed and on real data. Finally, once the R peaks of real data are detected, we use three methods of RR intervals analysis by calculating the standard deviation of heart rate and applying the Fast Fourier Transform FFT and the Wavelet Transform on detected RR intervals to study the Heart Rate Variability (HRV). A comparative study between the analysis results of detected RR intervals in healthy and diseased subjects through the application of the FFT and the Wavelet Transform will be given.

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A Robust R Peak Detection Algorithm Using Wavelet Transform for Heart Rate Variability Studies

A robust QRS complex detection using regular grammar and deterministic automata

Electroencephalography (EEG) is one of the most signals used for studying and demonstrating the electrical activity of the brain due to the absence of side effects, its noninvasive nature and its well temporal resolution. Indeed, it provides real-time information, so it can be easily suitable for predicting drivers’ vigilance states. The classification of these states through this signal requires sophisticated approaches in order to achieve the best prediction performance. Furthermore, deep learning (DL) approaches have shown a good performance in learning the high-level features of the EEG signal and in resolving classification issues. In this paper, we will predict individuals’ states of vigilance based on the study of their brain activity by analyzing EEG signals using DL architectures. In fact, we propose two types of networks: (i) a 1D-UNet model, which is composed only of deep one-dimensional convolutional neural network (1D-CNN) layers and (ii) 1D-UNet-long short-term memory (1D-UNet-LSTM) that combines the proposed 1D-UNet architecture with the LSTM recurrent model. The experimental results reveal that the suggested models can stabilize the training model, well recognize the subject vigilance states and compete with the state of art on multiple performance metrics. The per-class average of precision and recall can be, respectively, up to 86% with 1D-UNet and 85% with 1D-UNet-LSTM, hence the effectiveness of the proposed methods. In order to complete our virtual prototyping and to get a real evaluation of our alert equipment, these proposed DL models are implemented also on a Raspberry Pi3 device allowing measuring the execution time necessary for predicting the state vigilance in real time.

Asma Ben Abdallah

Papers

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

Real time QRS complex detection using DFA and regular grammar

A Robust R Peak Detection Algorithm Using Wavelet Transform for Heart Rate Variability Studies

A robust QRS complex detection using regular grammar and deterministic automata

Innovative deep learning models for EEG-based vigilance detection