Bailey Christopher
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6186-9. doi: 10.1109/EMBC.2015.7319805.
This paper presents a time-domain based lossless data reduction technique called Log2 Sub-band encoding, which is designed for reducing the size of data recorded on a wireless electroencephalogram (EEG) recorder. A data reduction unit can help to save power from the wireless transceiver and from the storage medium since it allows lower data transmission and read/write rates, and then extends the life time of the battery on the device. Our compression ratio(CR) results show that Log2 Sub-band encoding is comparable and even superior to Huffman coding, a well known entropy encoding method, whilst requiring minimal hardware resource, and it can also be used to extract features from EEG to achieve seizure detection during the compression process. The power consumption when compressing the EEG data is presented to evaluate the system0s overall improvement on its power performance, and our results indicate that a noticeable power saving can be achieved with our technique. The possibility of applying this method to other biomedical signals will also be noted.
本文提出了一种基于时域的无损数据缩减技术，称为对数2子带编码，该技术旨在减小无线脑电图（EEG）记录仪上记录的数据大小。数据缩减单元有助于节省无线收发器和存储介质的功耗，因为它允许更低的数据传输和读/写速率，从而延长设备上电池的使用寿命。我们的压缩率（CR）结果表明，对数2子带编码与著名的熵编码方法霍夫曼编码相当，甚至更优，同时所需的硬件资源最少，并且它还可用于从脑电图中提取特征，以便在压缩过程中实现癫痫检测。文中给出了压缩脑电图数据时的功耗，以评估系统在功率性能方面的整体改进，我们的结果表明，使用我们的技术可以实现显著的节能。还将指出将该方法应用于其他生物医学信号的可能性。
