Apnea recognition with wavelet neural networks

Apnea is a Sleep Disorder Syndrome characterized by an interruption or reduction of air flow for at least 10 seconds. Polysomnography is a test used to apnea diagnosis. Several signals, including Electrocardiogram (ECG), Electroencephalogram (EEG) and Oxygen Saturation (SpO2) are obtained in this diagnostic test. Since most tests for apnea are uncomfortable to the patients, there is an increase search for alternative methods to reduce cost and improve patient well-being. In this work, we use only SpO2 data from 25 patients of the St Vincent’s University Hospital, Dublin, to extract parameters connected to a Neural Network to classify patients with apnea or non-apnea. Results confirm that our alternative method can be used as an auxiliary tool for diagnosis by using exclusively SpO2 signal.Apneia é uma Desordem do Sono caracterizada pela interrupção ou redução do fluxo de ar por pelo menos 10 segundos. Seu diagnóstico é realizado principalmente com a Polissonografia, exame composto por uma série de sinais como o Eletrocardiograma (ECG), Electroencefalograma (EEG) e Saturação do Oxigênio (SpO2). A maioria dos exames para o diagnóstico da apneia são desconfortáveis e caros, incentivando a busca por métodos alternativos tanto para a redução do custo, quanto para o aumento do bem-estar do paciente. Neste trabalho, foi utilizado apenas dados advindos do SpO2 de 25 pacientes do St Vincent’s University Hospital, Dublin, para extrair parâmetros e alimentar uma Rede Neural, que realiza a classificação de pacientes com apneia. Os resultados confirmam que o método apresentado pode ser usado como uma ferramenta auxiliar no diagnóstico

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature