Serhani Mohamed Adel, Menshawy Mohamed El, Benharref Abdelghani, Harous Saad, Navaz Alramzana Nujum
College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE.
Concordia Institute for Information Systems Engineering, Concordia University, 1515 Rue Sainte-Catherine O, Montréal, QC, Canada, H3G 2W1, Canada.
Comput Methods Programs Biomed. 2017 Oct;149:79-94. doi: 10.1016/j.cmpb.2017.07.007. Epub 2017 Jul 22.
Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices.
In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments.
We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments.
Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.
微型生物医学传感器、移动智能手机、无线通信及分布式计算技术的最新进展为开发移动健康系统提供了有前景的技术。此类系统能够可靠地监测癫痫发作，癫痫发作被归类为慢性疾病。在此背景下，因使用移动设备持续记录癫痫发作而产生的大数据的转换、压缩、存储和可视化引发了三个具有挑战性的问题。
在本文中，我们通过开发三种新算法来严格且高效地处理和分析大量脑电图数据，以应对上述挑战。第一种算法负责将标准欧洲数据格式（EDF）转换为标准JavaScript对象表示法（JSON），并压缩转换后的JSON数据，以减小传输过程中的大小和时间，并提高网络传输速率。第二种算法专注于收集和存储由转换与压缩算法生成的压缩文件。收集过程是在解压文件后根据实时技术执行的。第三种算法为潜在用户提供与信号数据的相关实时交互。它特别具有以下功能：在智能手机设备上可视化单个或多个信号通道以及查询数据段。
我们通过实现一个用于监测癫痫发作的移动健康系统的软件架构模型，测试并评估了我们方法的有效性。来自45次实验的结果很有前景，并且在线性代价下有效地满足了该方法的目标。此外，在所有进行的实验中，压缩后的JSON文件大小和传输时间分别减少了10%和20%，而平均总时间显著减少了67%。
我们的方法在处理时间、内存使用和能耗方面成功开发了高效算法，同时保持了所提出解决方案的高可扩展性。我们的方法依靠MapReduce平台有效地支持数据分区和并行性，这有助于癫痫发作的监测和自动检测。
