A Sub-<inline-formula> <tex-math notation="LaTeX">$\mu\text{W}$ </tex-math> </inline-formula>/Channel, 16-Channel Seizure Detection and Signal Acquisition SoC Based on Multichannel Compressive Sensing

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

“…Data compression is a well-known method to reduce the power consumption of wireless transmitters in implantable electronic medical devices. Using data compression also reduces the power consumption of feature extractors as shown in [ 87 ]. Compressive sensing (CS) is a compression scheme presenting relevant characteristics for multi-channel neural recording.…”

“…The multi-channel compressive sensing (MCS) technique has been developed as a hardware-suitable compressive sensing that allows a straightforward circuit design as well as an efficient power × area performance [ 87 ]. Figure 22 a depicts the simplified concept of the MCS operating over N channels.…”

“…The complex data reconstruction operation is performed in the digital domain and involves a significant latency which would slow down any seizure pattern detection or further feature extraction processes. Feature extraction from spatially filtered data based on the MCS is presented in [ 87 ] to the aim of detecting seizures. A multi-input single-output compressive sensing (MISOCS) block carries out the projection of data originating from N channels into the compressed domain.…”

“…Figure 22 b presents the system architecture of a 16-channel ( ) conventional MISOCS block and the signals that control the corresponding circuit. A compression ratio of 16 ( ) is achieved by the system [ 87 ]. The 16 inputs to the MISOCS block originate from the front-end neural amplifiers differential outputs.…”

“…First, the random nature of the measurement matrix yields a random number of ones in the random sampling signals [ 87 ]. The offset of the summing amplifier depends on the number of ones, thus causing an error.…”