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The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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Central Sleep apnea is a serious condition that affects many individuals and is associated with severe health complications. During sleep, people with this condition stop breathing because the signals in the brain that tell the body to breathe don't work properly. There is no effort is made to inhale and chest movements almost come to a standstill. Central Sleep apnea is associated with a number of different neurologic problems, as well as heart or kidney failure. Existing medical sleep measurement systems are costly, disturb sleep quality, and are only suited for short-term measurement. As sleeping problems are affecting about 30% of the population [2] , new approaches for everyday sleep measurement are needed. This paper presents a simple, low cost measurement technique that involves use of a Force Sensing Resistor placed over the patient's chest to record chest movements and hence record the breathing signal. Analysis of the obtained Breathing signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of central sleep apnea, which may reduce the need for uncomfortable sleep studies. The proposed method involves usingWavelet Transform based feature extraction, followed by classification using Least Square Support Vector Machine . A 95% true detection rate was obtained using the proposed method. Finally, a real time breathing monitoring and Central Sleep Apnea diagnosis system is built using the proposed method.