A manifold learning framework for the detection of cardiac disorders in acoustic signals

Cardiac disorders are clinical situations in which the heart does not function properly. These disorders may be fatal to patients if they are not detected. Detecting such disorders often involves special and in some cases very expensive medical devices such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound imaging or Electrocardiograms. Acoustic detection of these disorders by simply listening to the heart using a stethoscope - although being the cheapest detection method - requires a highly skilled doctor. We propose a method that detects cardiac disorders from simple acoustic recordings of the heart. Acquiring such recording is in most cases cheaper than the above mentioned devices. The proposed algorithm is composed of two steps: an offline training step which constructs a classifier based on labeled recordings; and an online classification step which detects cardiac disorders given a recording of the heart. Given the online nature of the algorithm, the proposed algorithm can be implemented as a smartphone application. One of the key elements of oth the training and detection steps is the concise and informative representation of the acoustic signal. This representation is obtained using the application of the spline wavelet packet transform followed by the application of the Diffusion Maps (DM) dimensionality reduction algorithm. The proposed approach is generic and can be applied to various signal types for solving different classification problems.