scholarly journals Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 63
Author(s):  
Fatima Sajid Butt ◽  
Luigi La Blunda ◽  
Matthias F. Wagner ◽  
Jörg Schäfer ◽  
Inmaculada Medina-Bulo ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Fall Detection ◽  
Daily Activities ◽  
Continuous Wavelet ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Novel Approach ◽  
The Neural Network ◽  
Electrocardiogram Ecg

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

scholarly journals Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 935 ◽  
Author(s):  
Yeong-Hyeon Byeon ◽  
Sung-Bum Pan ◽  
Keun-Chang Kwak
Keyword(s):  
Frequency Domain ◽  
Training Transfer ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Noisy Signals ◽  
The Time Domain ◽  
Electrocardiogram Ecg

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG.

scholarly journals Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity

2020 ◽  
Author(s):  
Mika Hämäläinen ◽  
Niko Partanen ◽  
Khalid Alnajjar ◽  
Jack Rueter ◽  
Thierry Poibeau
Keyword(s):  
Transfer Learning ◽  
Word Association ◽  
Association Test ◽  
Learning Approaches ◽  
Word Association Test ◽  
Novel Approach ◽  
Evaluation Metric

We present a novel approach for adapting text written in standard Finnish to different dialects. We experiment with character level NMT models both by using a multi-dialectal and transfer learning approaches. The models are tested with over 20 different dialects. The results seem to favor transfer learning, although not strongly over the multi-dialectal approach. We study the influence dialectal adaptation has on perceived creativity of computer generated poetry. Our results suggest that the more the dialect deviates from the standard Finnish, the lower scores people tend to give on an existing evaluation metric. However, on a word association test, people associate creativity and originality more with dialect and fluency more with standard Finnish.

Deep Learning for Detecting Sleep Apnea from ECG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Ecg Signals ◽  
Novel Approach ◽  
Learning Machine ◽  
High Level ◽  
Electrocardiogram Ecg

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

Removal of ECG Baseline Wander Using a Resting Cycle Template

2021 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Eladio Altamira-Colado ◽  
Miguel Bravo-Zanoguera ◽  
Daniel Cuevas-González ◽  
Marco Reyna-Carranza ◽  
Roberto López-Avitia
Keyword(s):  
Spline Interpolation ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
New Method ◽  
Daily Activities ◽  
Pass Filter ◽  
Ecg Signals ◽  
Baseline Wander ◽  
Low Amplitude ◽  
Electrocardiogram Ecg

The development of electrocardiogram (ECG) wearable devices has increased due to its applications on ambulatory patients. ECG signals provide useful information about the heart behavior, but when daily activities are monitored, motion artifacts are introduced producing saturation of the signal, thus losing the information. The typical resolution used to record ECG signals is of maximum 16-bit, which might not be enough to detect low-amplitude potentials and at the same time avoid saturation due to baseline wander, since this last issue demands a low-gain signal chain. A high-resolution provides a more detailed ECG signal under a low gain input, and if the signal is corrupted by motion artifact noise but is not saturated, it can be filtered to recover the signal of interest. In this work, a 24-bit ADC is used to record the ECG, and a new method, the rest ECG cycle template, is proposed to remove the baseline wander. This new method is compared to high-pass filter and spline interpolation methods in their ability to remove baseline wander. This new method presumes that a user is able to establish a rest ECG during his/her daily activities.

Developing a smart walking cane with remote electrocardiogram and fall detection

2021 ◽  
pp. 1-14
Author(s):  
Hsi-Chiang Chou ◽  
Kai-Yu Han
Keyword(s):  
Health Care Professionals ◽  
Reference Data ◽  
Numerical Data ◽  
Fall Detection ◽  
Human Machine Interface ◽  
Wireless Transmission ◽  
Ecg Signals ◽  
Detection Circuit ◽  
Machine Interface ◽  
Electrocardiogram Ecg

This study developed a smart cane with remote electrocardiogram (ECG) and fall detection. The cane comprises a self-developed ECG detection circuit, fall detection module composed of a three-axis gyroscope and three-axis accelerator, and two wireless transmission modules. The data reception end features a human–machine interface with self-developed ECG analysis and fall detection programs, providing reference data for identifying an abnormal situation. The hardware of the proposed system is divided into two parts. First, ECG detection is achieved using a copper column-shaped detector in place of conventional ECG electrodes. The self-developed sensor circuit amplifies the collected signals and filters unwanted noise to generate complete ECG signals. An Arduino MEGA microcontroller board and the two wireless transmission modules then transmit the signals to the human–machine interface. Second, fall detection is achieved using the aforementioned fall detection module to collect numerical data, which are then transmitted to the human–machine interface through the Arduino MEGA and wireless transmission modules. The proposed system can be applied to real-time monitoring and provide reference data for health care professionals and nursing personnel.

scholarly journals Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 662
Author(s):  
Jonathan Moeyersons ◽  
John Morales ◽  
Amalia Villa ◽  
Ivan Castro ◽  
Dries Testelmans ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Detection Model ◽  
Detection Algorithms ◽  
Artefact Detection ◽  
Important Diagnostic Tool ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

scholarly journals Deep Learning in Physiological Signal Data: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 969 ◽  
Author(s):  
Beanbonyka Rim ◽  
Nak-Jun Sung ◽  
Sedong Min ◽  
Min Hong
Keyword(s):  
Deep Learning ◽  
Signal Analysis ◽  
Medical Application ◽  
Learning Task ◽  
Physiological Data ◽  
Learning Approaches ◽  
Physiological Signal ◽  
Novel Approach ◽  
Model Training ◽  
Electrocardiogram Ecg

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System

2016 ◽  
Vol 25 (09) ◽  
pp. 1650103 ◽  
Author(s):  
Jing Hua ◽  
Hua Zhang ◽  
Jizhong Liu ◽  
Junlong Zhou
Keyword(s):  
Compressive Sensing ◽  
Single Channel ◽  
Temporal Correlation ◽  
Reconstruction Algorithm ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Reconstruction Performance ◽  
Low Energy Consumption ◽  
Spatio Temporal ◽  
Electrocardiogram Ecg

Due to the capacity of compressing and recovering signal with low energy consumption, compressive sensing (CS) has drawn considerable attention in wireless telemonitoring of electrocardiogram (ECG) signals. However, most existing CS methods are designed for reconstructing single channel signal, and hence difficult to reconstruct multichannel ECG signals. In this paper, a spatio-temporal sparse model-based algorithm is proposed for the reconstruction of multichannel ECG signals by not only exploiting the temporal correlation in each individual channel signal, but also the spatial correlation among signals from different channels. In addition, a dictionary learning (DL) approach is developed to enhance the performance of the proposed reconstruction algorithm by using the sparsity of ECG signals in some transformed domain. The approach determines a dictionary by learning local dictionaries for each channel and merging them to form a global dictionary. Extensive simulations were performed to validate the proposed algorithms. Simulation results show that the proposed reconstruction algorithm has a better performance in recovering multichannel ECG signals as compared to the benchmarking methods. Moreover, the reconstruction performance of the algorithm can be further improved by using a dictionary matrix, which is obtained from the proposed DL algorithm.

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