Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges
SK Saini, R Gupta - Artificial Intelligence Review, 2022 - Springer
Abstract Cardiovascular diseases (CVDs) in India and globally are the major cause of
mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace …
mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace …
Time–frequency signal processing: Today and future
Most real-life signals exhibit non-stationary characteristics. Processing of such signals
separately in the time-domain or in the frequency-domain does not provide sufficient …
separately in the time-domain or in the frequency-domain does not provide sufficient …
IoT-enabled WBAN and machine learning for speech emotion recognition in patients
Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging
technology that combines medical devices, wireless devices, and non-medical devices for …
technology that combines medical devices, wireless devices, and non-medical devices for …
Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals
AF Quiceno-Manrique, JI Godino-Llorente… - Annals of biomedical …, 2010 - Springer
This work discusses a method for the selection of dynamic features, based on the calculation
of the spectral power through time applied to the detection of systolic murmurs from …
of the spectral power through time applied to the detection of systolic murmurs from …
A comparative investigation of automatic speech recognition platforms for aphasia assessment batteries
SS Mahmoud, RF Pallaud, A Kumar, S Faisal, Y Wang… - Sensors, 2023 - mdpi.com
The rehabilitation of aphasics is fundamentally based on the assessment of speech
impairment. Developing methods for assessing speech impairment automatically is …
impairment. Developing methods for assessing speech impairment automatically is …
[HTML][HTML] Time-frequency distributions of heart sound signals: A comparative study using convolutional neural networks
Abstract Time-Frequency Distributions (TFDs) support the heart sound characterisation and
classification in early cardiac screening. However, despite the frequent use of TFDs in signal …
classification in early cardiac screening. However, despite the frequent use of TFDs in signal …
Application of parameterized time-frequency analysis on multicomponent frequency modulated signals
Parameterized time-frequency (TF) transforms, with signal-dependent kernel parameters,
have been proposed to analyze multicomponent frequency modulated (FM) signals. Usually …
have been proposed to analyze multicomponent frequency modulated (FM) signals. Usually …
An efficient deep learning based method for speech assessment of mandarin-speaking aphasic patients
SS Mahmoud, A Kumar, Y Tang, Y Li… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Speech assessment is an important part of the rehabilitation process for patients with
aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone …
aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone …
Performance evaluation of machine learning frameworks for aphasia assessment
Speech assessment is an essential part of the rehabilitation procedure for patients with
aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate …
aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate …
The application of the Hilbert spectrum to the analysis of electromyographic signals
This paper investigates the application of the Hilbert spectrum (HS), which is a recent tool for
the analysis of nonlinear and nonstationary time-series, to the study of electromyographic …
the analysis of nonlinear and nonstationary time-series, to the study of electromyographic …