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Identification and Removal of Noise in Cardiac Signals (CARDIO-NOISE)

The increase of population ageing and sedentary lifestyle of today's society leads to a greater demand for hospital services, which are unable to efficiently balance the demand with the supply [1]. This problem led to the development of automatic systems able to collect vital information from subjects on a daily basis, and through these, evaluate possible pathologies without the need for patients to request hospital services, thus, leading to a relief in demand and cost of medical consultations. These automatic systems are called tele-monitoring systems, which are integrated with biosensors to acquire biosignals and computational algorithms to process them. These algorithms are mathematical methodologies which provide the necessary intelligence to the system in order to detect diseases and physiological information about a given subject. However, due to the ambulatory nature of such systems, the acquisition of bio-signals is exposed to numerous sources of noise, leading to the signals contamination. Noise can lead to wrong event algorithm behavior, therefore, in order to prevent pathological false detections it is essential to make the detection of contaminated periods. The focus of this thesis is noise detection in bio-signals, particularly in Phonocardiogram (PCG) and Electrocardiogram (ECG). The noise detection methodology in the PCG context is characterized by being a real-time and multi-channel process. It can be divided into two phases: a first phase consists in searching a clean heart sound (HS) reference; the second phase compares this reference with the remaining test windows and evaluates the presence of noise based on the spectral similarity and the ratio of the total amount of high frequency components, between the reference and test windows. In healthy signals the algorithm achieved a sensitivity and specificity of 91.24% and 90.88%, respectively. In pathological signals it reached a specificity of 91.68%. Its computational cost is 0.17s per minute of PCG signal at 4000Hz. It was also compared on the same testing dataset with the methodologies that presented the highest precision rates in literature, namely the Modulation Filtering algorithm [2] and the Periodicity Based algorithm [3]. Additionally to the better results presented by our algorithm, it was also more computationally efficient than the compared methodologies.