A Multi-class ECG Beat Classifier Based on the Truncated KLT Representation

Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio

doi:10.1109/ems.2014.31

Cited by 23 publications

(6 citation statements)

References 13 publications

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“…The use of support vector machine (SVM) method and adaboost such as detection of ventricular fibrillation rhythm by using a supported vector machine amplified with optimal combination of variables [34]. Two novel methods for multiclass ECG arrhythmias classification based on principal component analysis (PCA), fuzzy support vector machine and unbalanced clustering [35], A multi-class ECG beat classifier based on the truncated a truncated karhunen-loeve transform (KLT) Representation [36], multiclass ECG beat classification based on a gaussian mixture model of karhunen-loève transform [37], Research on rhythmic ventricular fibrillation (VF) detected using a new approach involving support vector machine algorithm SVM, adaptive enhancement (Boost) and evolutionary differential algorithm (DE) with the help of optimal combination of variables. The end of our method is that it takes up less memory and can be implemented in real-time.…”

Section: Introductionmentioning

confidence: 99%

Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm

Sumiati

Repi

Hendriyati³

et al. 2023

IJ-AI

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<p>The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.</p>

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Section: Introductionmentioning

confidence: 99%

Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm

Sumiati

Repi

Hendriyati³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Plawiak [5], tested probabilistic neural network (PNN), radial basis function network (RBFNN), k-nearest neighbor (KNN), and support-vector machines (SVM) with genetic algorithm-based optimization (GA). In [10], [11], authors used a Gaussian mixture model of Karhunen-Loève transform. In [5], the authors combined the genetic algorithm with SVM.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based cardio-vascular disease diagnosis system

Hamdan

Aksa

Omari

et al. 2022

IJEECS

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Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance

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“…An accurate detection of activities can be reached by acquiring information both on acceleration and other biosignals such as ECG and EMG signals at the same time [7,8]. With these On the one hand, with the progress of the signal processing techniques, more and more information can be derived from biosignals [19][20][21][22][23]. It has been widely demonstrated how capturing EMG and ECG signals of a person can efficiently and easily assess its health status [24,25], body posture [26], fitness level [27], and physical performance [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Biagetti¹,

Crippa²,

Falaschetti³

et al. 2020

Electronics

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This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide a comprehensive stream of data suitable for human activity detection, motion analysis, and technology-assisted nursing of persons with physical or cognitive impairments. The sensor is able to acquire up to three independent bioelectrical channels (six electrodes), each with 24 bits of resolution and a sampling rate up to 3.2 kHz, and has a 6-DoF inertial platform measuring linear acceleration and angular velocity. The bluetooth low-energy wireless link was chosen because it allows easy interfacing with many consumer electronics devices, such as smartphones or tablets, that can work as data aggregators, but also imposes data rate restrictions. These restrictions are investigated in this paper as well, together with the strategy we adopted to maximize the available bandwidth and reliability of the transmission within the limits imposed by the protocol.

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A Multi-class ECG Beat Classifier Based on the Truncated KLT Representation

Cited by 23 publications

References 13 publications

Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm

Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm

A deep learning-based cardio-vascular disease diagnosis system

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

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