The reduction of transmitted data may also result in a loss of signal content, which can later impact the seizure detection performance. A thorough analysis that takes into account the power consumption of the microcontroller and the wireless transmitter on the sensor unit and the seizure detection performance is hence crucial when developing data reduction techniques for a wireless seizure detection system. Such analysis has not been considered in previous works.In this paper, we present energy-efficient data reduction approaches for reducing transmission data in a wireless EEG seizure detection system. Specifically, we look at two data reduction approaches, compressive sensing-based EEG compression and low-complexity feature extraction and transmission.

The performance is quantified in terms of seizure detection effectiveness and power consumption. The goal is to assess the use of data reduction methods for minimizing the power consumption at the sensor side, while maintaining high seizure detection performance. The tradeoffs between seizure detection performance and power consumption when choosing system parameters are also discussed. The paper is organized as follows: In Section 2, we present the setup of a wireless seizure detection system and describe different data reduction approaches. In Section 3, we describe the evaluation methodologies for assessing seizure detection effectiveness and the system power consumption of each approach. In Sections 4 and 5, we present the analysis results and discuss the tradeoffs. Finally, the conclusion is presented in Section 6.

2.?Data Transmission in a Wireless Seizure Detection SystemA generic wireless EEG system is comprised of two subsystems: a wireless EEG sensor node and a data server. The sensor node captures the EEG signals and transmits them to the data server via a wireless link. The sensor node consists of a data acquisition module, a microcontroller, a flash memory Carfilzomib module, a wireless transmitter and a battery pack. The data server, typically a standard personal computer, receives the signals and processes them. The wireless transmission of the EEG signals from the sensor node to the data server constitutes a major source of power consumption on the sensor node. By performing data compression or feature extraction on the raw EEG data on the sensor node, the amount of data that needs to be transmitted and, hence, the required power consumption can be substantially reduced.

In the following, we describe the conventional approach of transmitting the entire EEG signals and two different data reduction methods, namely EEG compression and feature extraction, in the context of a wireless seizure detection system (see Figure 1).Figure 1.Transmission of electroencephalogram (EEG) in a wireless seizure detection system. (Top) The entire EEG signals are transmitted.