Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation

Martis, Roshan Joy; Acharya, U. Rajendra; Adeli, Hojjat; Prasad, Hari; Tan, Jen Hong; Chua, Kuang Chua; Too, Chea Loon; Yeo, Sharon; Tong, Louis

doi:10.1016/j.bspc.2014.04.001

Cited by 90 publications

(43 citation statements)

References 72 publications

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“…The resulting technologies aim to help patients monitor their own conditions while also supporting carer-providers. Examples include the detection and monitoring of stress [47,62,68,74,83], classification of different emotional states [18,20,40,41], depression monitoring [7,37,73], obsessive compulsive disorder [12], behaviour classification [55], or cardiac states [48,49,65].…”

Section: Related Workmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

155

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2 Mozos et al.Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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Section: Related Workmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

155

View full text Add to dashboard Cite

show abstract

“…Datasets with the same label form one signal class. For example, ECG signals from normal controls (Normal Sinus Rhythm (NSR)) form the N SR signal class [15]. ECG signals taken from patients with Atrial Knowing the signal labels allows us to assess the algorithms used in the offline system.…”

Section: Ecg Signalsmentioning

confidence: 99%

“…For example, the hear rate variability is such a quantity of interest [25] • First and second order statistics -Mean and variance [26] • Discrete Wavelet Transform (DWT) -This feature extraction technique is closely related to spectrum techniques. Spectrum techniques provide only frequency restitution, whereas DWT provides both time and frequency resolution [27,28,27,29] • Independent Component Analysis (ICA) -The technique separates multivariate signals into their additive subcomponents [27,15,28] • Principal Component Analysis (PCA) -The statistical procedure is based on orthogonal transformation which produces linearly uncorrelated parameters known as the principal components [30,29,28,31,32] • Linear Discriminant Analysis (LDA) -Yields features that characterizes two or more signal classes [28] • Discrete Cosine Transform (DCT) -Spectrum technique based on cosine waves [15,33] Nonlinear methods are based on the more recent concepts of chaos and fuzzy logic [34,35,36]. The novelty of these methods is reflected in the fact that only a few recent CAD systems employ them, as indicated in the following list.…”

Section: Feature Extractionmentioning

confidence: 99%

“…• Decision Tree (DT) -The decision making algorithm is based on a tree like graph that structures a continuum of binary questions [51,52,15,24].…”

Section: Classificationmentioning

confidence: 99%

“…• K-Nearest Neighbour (K-NN) -The majority of neighboring known signal points decides the signal class of an unknown input [15,28,31].…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Faust

2016

J. Mech. Med. Biol.

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The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.

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EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features

Luo

Jin³

et al. 2021

Artificial Intelligence

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Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation

Cited by 90 publications

References 72 publications

Stress Detection Using Wearable Physiological and Sociometric Sensors

Stress Detection Using Wearable Physiological and Sociometric Sensors

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features

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