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Title: Low-overhead Compression Of Ecg Recordings For Implantable Medical Devices
Authors: Αντώνιος Σωμαράκης
Σούντρης Δημήτριος
Keywords: implantable medical devices (imds)
electrocardiography (ecg)
data compression
ecg compression ratio
low overhead
percent root difference (prd)
compression ratio
Issue Date: 28-Mar-2016
Abstract: It is an indisputable fact that Implantable Medical Devices (IMDs) are becoming an integral part of Medical science. IMDs are encountered in a great variety of medical applications. IMDs rely on data acquisition, processing andcommunication agents in order to sustain and ameliorate the life of the patients. IMDs have limited memory, computational and battery power resources, while collecting, processing and transmitting out information frompotentially many sensors. These limitations require that information within the devices be efficiently compressed. Such data compression presents a challenging task, as it must provide high fidelity of the waveform reproductionand high compression ratios on limited size data frames. Also, it must be based on the type of data to be compressed, in order to provide bigger efficiency. Inthis thesis we try to better up the existing lossy and lossless compression methods. In order to manage that, we use various algorithms and combinations of those in order to find the most efficient scheme. The two main algorithmsthat we use are LZO encoding algorithm and SPIHT encoding algorithm. We combine these encoding algorithms with various data procession algorithms. Our main attempt is to evaluate the aforementioned algorithms and so we useElectrocardiography (ECG), an extremely widely used biodata which is recorded from IMDs and sent or saved from them. The main evaluation parameters of our thesis are the compression ratio, the Percent Root mean square Difference (PRD) and computational overhead of each algorithm.Finally, based on the evaluation process we conclude that SPIHT with Reordering with fuzzy C means Clustering offer the best compression ratio 25.95 with RPD 4.86 and the best tradeoff between compression ratio and PRD the LZO with Reordering technique with 10.67 compression ratio and 3.13 .As for the lossless algorithms LZO with Reordering with fuzzy C means clustering offers 2.42 compression ratio.
Appears in Collections:Διπλωματικές Εργασίες - Theses

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