A Low Power System With EEG Data Reduction for Long-Term Epileptic Seizures Monitoring

• Published in: IEEE access Vol. 7; pp. 71195 - 71208
• Main Authors: Imtiaz, Syed Anas, Iranmanesh, Saam, Rodriguez-Villegas, Esther
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical monitoring; Bluetooth; CMOS; Convulsions & seizures; Data reduction; Electroencephalography; Epilepsy; Feature extraction; Idling; Integrated circuits; low-power biomedical system; Monitoring; Power consumption; Power integrated circuits; Real-time systems; seizure detection; Seizures; Telemedicine; wearables
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2920006

Long-term monitoring of epilepsy patients requires low-power systems that can record and transmit electroencephalogram data over extended periods of time. Since seizure events are rare, long-term monitoring inherently results in large amounts of data that are recorded and hence need to be reduced. This paper presents an ultra-low power integrated circuit implementation of a data reduction algorithm for epilepsy monitoring, specific to seizure events. The algorithm uses line length of the electroencephalogram signals as the key discriminating feature to classify epochs of data as seizure or non-seizure events. It is implemented in AMS 0.18-<inline-formula> <tex-math notation="LaTeX">\mu \text{m} </tex-math></inline-formula> CMOS technology and its output is connected to a Bluetooth low energy transceiver to wirelessly transmit potential seizure events. All the modules of the algorithm have been implemented on chip to use a small number of clock cycles and remain mostly in an idle mode. The algorithm, on the chip, achieves 50% of data reduction with a sensitivity of 80% for capturing seizure events. The overall power consumption of the chip is measured to be 23 <inline-formula> <tex-math notation="LaTeX">\mu \text{W} </tex-math></inline-formula>, while the full system with wireless transmission consumes 743 <inline-formula> <tex-math notation="LaTeX">\mu \text{W} </tex-math></inline-formula>. The results in this paper demonstrate the feasibility of a long-term seizure monitoring system capable of running autonomously for over two weeks.