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2 2022

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Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals

Gupta Kapil, Bajaj Varun, Ansari Irshad Ahmad, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

784--796

Hypertension (HPT) is a physiological abnormality characterized by high blood pressure, headache, wooziness, and fainting that may lead to various heart, kidney, or brain diseases. Detection and continuous monitoring of HPT by sphygmometer is arduous and hectic. Nowadays, ballistocardiogram (BCG) signals are used to determine HPT as it indicates the vibration of the heart. Usual linear and nonlinear hand-crafted machine learning methods are subjective, involve decomposition of signal, features elicitation, selection, and classification steps. In this work, a completely automated HPT detection system is proposed using time–frequency (T-F) spectral images and a convolutional neural network (CNN) for the accurate detection of HPTusing BCG signals. The BCG signals are subjected to Gabor transform (GT), smoothed pseudo-Wigner Ville distribution (SPWVD), and short-time Fourier transform (STFT) techniques to obtain T-F spectral images. These T-F spectral images are fed to benchmarked Alex-Net, Res-Net50, and proposed CNN (Hyp-Net) to develop the automated HPT detection model with a 10-fold cross-validation scheme. The proposed Hyp-Net obtained the highest detection accuracy of 97.65% with GT-based spectral images. In comparison to Alex-Net and Res-Net50 pre-trained models, our developed Hyp-Net needs minimal learnable parameters, makes it computationally fast and more efficient. This shows that our proposed model has outperformed other two transfer learning methods. The experimental results with collected BCG signals from a public dataset are provided to show the effectiveness of the presented technique for automated detection of HPT.

An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients

Karan Biswajit, Sekhar Sahu Sitanshu, Orozco-Arroyave Juan Rafael

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

710--726

Parkinson’s disease (PD) is the most common neurological disorder that typically affects elderly people. In the earlier stage of disease, it has been seen that 90% of the patients develop voice disorders namely hypokinetic dysarthria. As time passes, the severity of PD increases, and patients have difficulty performing different speech tasks. During the progression of the disease, due to less control of articulatory organs such as the tongue, jaw, and lips, the quality of speech signals deteriorates. Periodic medical evaluations are very important for PD patients; however, having access to a medical appointment with a neurologist is a privilege in most countries. Considering that the speech recording process is inexpensive and very easy to do, we want to explore in this paper the suitability of mapping information of the dysarthria level into the neurological state of patients and vice versa. Three levels of severity are considered in a multiclass framework using time-frequency (TF) features and random-forest along with an Error-Correcting Output Code (ECOC) approach. The multiclass classification task based on dysarthria level is performed using the TF features with words and diadochokinetic (DDK) speech tasks. The developed model shows an unweighted average recall (UAR) of 68.49% with the DDK task /pakata/ based on m-FDA level, and 48.8% with the word /petaka/ based on the UPDRS level using the Random Forest classifier. With the aim, to evaluate the neurological states using the dysarthria level, the developed models are used to predict the MDS-UPDRS-III level of patients. The highest matching accuracy of 32% with the word /petaka/ is achieved. Similarly, the multiclass classification framework based on MDS-UPDRS-III is applied to predict the dysarthria level of patients. In this case, the highest matching accuracy of 18% was obtained with the DDK tasks /pataka/.