Deep learning for processing electromyographic signals: A taxonomy-based survey

Buongiorno, Domenico; Cascarano, Giacomo Donato; Feudis, Irio De; Brunetti, Antonio; Carnimeo, Leonarda; Dimauro, Giovanni; Bevilacqua, Vitoantonio

doi:10.1016/j.neucom.2020.06.139

Cited by 55 publications

(31 citation statements)

References 191 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Historically, EMG analysis methods evolved from spectral analysis [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and time-domain signal analysis methods such as morphological analysis [ 8 ], amplitude analysis [ 9 ], and autoregressive analysis [ 10 , 11 ] towards time–frequency domain analysis [ 12 , 13 , 14 , 15 , 16 ]. The state-of-the-art of EMG analysis methods is characterized by the active use of nonlinear data analysis methods [ 17 ], such as fractal analysis [ 18 ], phase analysis [ 19 ], recurrent quantification analysis [ 4 , 20 , 21 ], and the deep learning of neural networks [ 12 , 22 , 23 , 24 , 25 ]. According to the authors, the existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis [ 26 , 27 , 28 ], focus on local time–frequency changes in the signal and, therefore, do not reveal the generalized properties of the signal.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

Sushkova

Morozov

Gabova

et al. 2021

Sensors

View full text Add to dashboard Cite

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

show abstract

Section: Introductionmentioning

confidence: 99%

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

Sushkova

Morozov

Gabova

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Systems able to support the doctors’ work in the diagnosis of pathologies can facilitate health care decision making reducing considerably expenditure of time and money [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. The ECG has become the diagnostic procedure most commonly performed in clinical cardiology [ 13 , 14 , 15 ] and the diffusion of wearable and portable devices has been enabling patients to constantly monitor the cardiac activity, for example of elder people through wireless sensor networks [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

et al. 2021

Self Cite

View full text Add to dashboard Cite

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

show abstract

“…In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes' posture is conducive to accurately judging sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes' performance and reducing physical damage. At present, human motion posture capture technology is mainly based on three methods: visual images [1][2][3][4], electromyographic signals [5][6][7], and wearable inertial sensors [8][9][10]. Among them, the human body gesture capture technology based on wearable inertial sensors has the advantages of directness, reliability, and strong applicability.…”

Section: Introductionmentioning

confidence: 99%

Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors

Liu

Yuan

2021

Scientific Programming

View full text Add to dashboard Cite

In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judge sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage. The manufacturing level of MEMS sensors continues to improve, and status perception of assembly objects is becoming more and more abundant and accurate. The shape is small and can be worn, and data can be collected continuously without obstacles. The price is relatively low, the privacy protection is strong, and the advantages are clear and prominent. A considerable number of technicians choose to use MEMS sensors as the main tool for human behavior detection data collection. Therefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer. In order to make the obtained acceleration value, angular velocity value, and magnetometer value accurately reflect the movement state of the lower limb structure, different data fusion algorithms and magnetometer ellipsoid fitting calibration algorithms are studied to realize the calculation of the posture angle of each joint point and obtain martial arts posture big data. In addition, through big data analysis, this article designs a martial arts training performance and injury risk prediction model, which can provide guidance and suggestions for martial arts teaching tasks.

show abstract

Deep learning for processing electromyographic signals: A taxonomy-based survey

Cited by 55 publications

References 191 publications

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors

Contact Info

Product

Resources

About