Healthcare Technology Letters
Volume 7, Issue 6, December 2020
Volumes & issues:
Volume 7, Issue 6
December 2020
Mathematical model of the human respiratory system in chronic obstructive pulmonary disease
- Author(s): Fleur T. Tehrani
- Source: Healthcare Technology Letters, Volume 7, Issue 6, p. 139 –145
- DOI: 10.1049/htl.2020.0060
- Type: Article
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Chronic obstructive pulmonary disease (COPD) is a respiratory illness with high rates of morbidity and mortality. The human respiratory system undergoes many chronic changes and adaptations under this disease conditions. The purpose of this study was to devise a mathematical model of the human respiratory system under COPD. The model presented is based on a previous detailed model of the human respiratory system. Cyclic changes of the lung volume with the effects of increased dead space are included and the lung mechanics are used to adjust the rate of breathing. Continuous and dynamic changes in the cardiac output and cerebral blood flow are represented in the model. The model includes the modified response of the respiratory control system under the chronic effects of COPD including the shifting of the acid–base balance under the disease conditions. The performance of the model has been examined at rest and during moderate exercise with and without oxygen supplementation. The results under different stimuli are found to be in general agreement with experimental observations.
Explainable artificial intelligence for heart rate variability in ECG signal
- Author(s): Sanjana K. ; Sowmya V. ; Gopalakrishnan E.A. ; Soman K.P.
- Source: Healthcare Technology Letters, Volume 7, Issue 6, p. 146 –154
- DOI: 10.1049/htl.2020.0033
- Type: Article
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Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.
Automated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis
- Author(s): Ato Kapfo ; Samarendra Dandapat ; Prabin Kumar Bora
- Source: Healthcare Technology Letters, Volume 7, Issue 6, p. 155 –160
- DOI: 10.1049/htl.2020.0015
- Type: Article
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In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.
Characterisation of black skin stratum corneum by digital macroscopic images analysis
- Author(s): Géraud M. Azehoun-Pazou ; Kokou M. Assogba ; Hugues Adegbidi ; Antoine C. Vianou
- Source: Healthcare Technology Letters, Volume 7, Issue 6, p. 161 –167
- DOI: 10.1049/htl.2020.0057
- Type: Article
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Black skin medical images generally show very low contrast. Being in a global initiative of characterisation of black skin horny layer (stratum corneum) by digital images analysis, the authors in this study proposed a four-step approach. The first step consists of differentiation between probable healthy skin regions and those affected. For that, they used an automatic classification system based on multilayer perceptron artificial neural networks. The network has been trained with texture and colour features. Best features selection and network architecture definition were done using sequential network construction algorithm-based method. After classification, selected regions undergo a colour transformation, in order to increase the contrast with the lesion region. Thirdly, created colour information serves as the basis for a modified fuzzy c-mean clustering algorithm to perform segmentation. The proposed method, named neural network-based fuzzy clustering, was applied to many black skin lesion images and they obtained segmentation rates up to 94.67%. The last stage consists in calculating characteristics. Eight parameters are concerned: uniformity, standard deviation, skewness, kurtosis, smoothness, entropy, and average pixel values calculated for red and blue colour channels. All developed methods were tested with a database of 600 images and obtained results were discussed and compared with similar works.
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