Muhammad Tariqus Salam

A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector

A novel implantable low-power integrated circuit is proposed for real-time epileptic seizure detection. The presented chip is part of an epilepsy prosthesis device that triggers focal treatment to disrupt seizure progression. The proposed chip integrates a front-end preamplifier, voltage-level detectors, digital demodulators, and a high-frequency detector. The preamplifier uses a new chopper stabilizer topology that reduces instrumentation low-frequency and ripple noises by modulating the signal in the analog domain and demodulating it in the digital domain. Moreover, each voltage-level detector consists of an ultra-low-power comparator with an adjustable threshold voltage. The digitally integrated high-frequency detector is tunable to recognize the high-frequency activities for the unique detection of seizure patterns specific to each patient. The digitally controlled circuits perform accurate seizure detection. A mathematical model of the proposed seizure detection algorithm was validated in Matlab and circuits were implemented in a 2 mm2 chip using the CMOS 0.18- μm process. The proposed detector was tested by using intracerebral electroencephalography (icEEG) recordings from seven patients with drug-resistant epilepsy. The seizure signals were assessed by the proposed detector and the average seizure detection delay was 13.5 s, well before the onset of clinical manifestations. The measured total power consumption of the detector is 51 μW.

Low-Power Implantable Device for Onset Detection and Subsequent Treatment of Epileptic Seizures: A Review

Over the past few years, there has been growing interest in neuro-responsive intracerebral local treatments of seizures, such as focal drug delivery, focal cooling, or electrical stimulation. This mode of treatment requires an effective intracerebral electroencephalographic acquisition system, seizure detector, brain stimulator, and wireless system that consume ultra-low power. This review focuses on alternative brain stimulation treatments for medically intractable epilepsy patients. We mainly discuss clinical studies of long-term responsive stimulation and suggest safer optimized therapeutic options for epilepsy. Finally, we conclude our study with the proposed low-power, implantable fully integrated device that automatically detects low-voltage fast activity ictal onsets and triggers focal treatment to disrupt seizure progression. The detection performance was verified using intracerebral electroencephalographic recordings from two patients with epilepsy. Further experimental validation of this prototype is underway.

An Implantable Closedloop Asynchronous Drug Delivery System for the Treatment of Refractory Epilepsy

In this paper, we present an implantable device for intra-cerebral electroencephalography (icEEG) data acquisition and real-time epileptic seizure detection with simultaneous focal antiepileptic drug injection feedback. This implantable device includes a neural signal amplifier, an asynchronous seizure detector, a drug delivery system (DDS) including a micropump, and a hybrid subdural electrode (HSE). The asynchronous detection algorithm is based on data-dependent analysis and validated with Matlab tools. The detector and DDS have a power saving mode. The HSE contacts are made of Platinum (Pt) encapsulated with polydimethylsiloxane (PDMS). Given the heterogeneity of electrographic seizure signals and seizure suppression threshold, the implantable device provides tunable parameters facility through an external transmitter to adapt to each individuals neurophysiology prior to clinical deployment. The proposed detector and DDS were assembled in Ø 50 mm and Ø 30 mm circular printed circuit boards, respectively. The detector was validated using icEEG recordings of seven patients who had previously undergone an intracranial investigation for epilepsy surgery. The triggering of the DDS was tested and a predefined seizure suppression dose was delivered ~16 s after electrographical seizure onsets. The devices power consumption was reduced by 12% in active mode and 49% in power saving mode compared to similar seizure detection algorithms implemented with synchronous architectureAn implantable closedloop asynchronous drug delivery system for the treatment of refractory epilepsy

A Fully-Asynchronous Low-Power Implantable Seizure Detector for Self-Triggering Treatment

In this paper, we present a new asynchronous seizure detector that is part of an implantable integrated device intended to identify electrographic seizure onset and trigger a focal treatment to block the seizure progression. The proposed system has a low-power front-end bioamplifier and a seizure detector with intelligent mechanism to reduce power dissipation. This system eliminates the unnecessary clock gating during normal neural activity monitoring mode and reduces power dissipation in the seizure detector; as a result, this device is suitable for long-term implantable applications. The proposed system includes analog and digital building blocks with programmable parameters for extracting electrographic seizure onset information from real-time EEG recordings. Sensitivity of the detector is enhanced by optimizing the variable parameters based on specific electrographic seizure onset activities of each patient. The detection algorithm was validated using Matlab tools and implemented in standard 0.13 μm CMOS process with total die area of 1.5×1.5 mm2. The fabricated chip is validated offline using intracranial EEG recordings from two patients with refractory epilepsy. Total power consumption of the chip is 9 μW and average detection delay is 13.7 s after seizure onset, well before the onset of clinical manifestation. The proposed system achieves an accurate detection performance with 100% sensitivity and no false alarms during the analyses of 15 seizures and 19 non-seizure datasets.

Low-power CMOS-based epileptic seizure onset detector

In this paper, we present an implantable CMOS integrated device that automatically detects epileptic seizure onsets. By recognizing partial-onset seizures, it can improve epilepsy treatment. The circuit consists of a chopper stabilized preamplifier, comprising a modulator, an amplifier, a high-pass filter with low cut-off frequency, and a voltage span detector. The proposed low-power detector extracts seizure onset information from neural signals and monitors the signals over a time period to capture seizure events. Signals are analyzed and the mathematical model is validated in Matlab. Circuits are implemented in a CMOS 0.18-µm process. Total power consumption of the preamplifier and detector are 6.72 uW and 55.51 nW, respectively. Detection performance was verified using intracerebral electroencephalographic recordings from epileptic patients, and the detector accurately identified seizure onsets.

Epileptic low-voltage fast-activity seizure-onset detector

In this paper, we present a seizure detector that is part of an implantable CMOS integrated device intended to identify seizure onsets and trigger focal treatment to disrupt seizure progression. The detector consists of a preamplifier, voltage level detectors, digital demodulators and a high-frequency detector. Variable gain amplification, adjustable threshold voltage identification and tunable recognition of high-frequency activities provide unique detection criteria for a specific patient. Moreover, digitally-controlled low-power CMOS circuits perform accurate seizure detection. A mathematical model of the seizure detection algorithm was validated in Matlab and circuits were implemented in a CMOS 0.18-µm process. Total power consumption of the detector is 6.71 µW. Detection performance was verified using intracerebral electroencephalographic recordings from a patient with epilepsy.

Combined NIRS-EEG remote recordings for epilepsy and stroke real-time monitoring

In this paper, we present system design of remote data recording for epileptic and stroke patients. We report wireless recording system combining near infra-red spectrometry (NIRS) in a comprehensive non-invasive evaluation and both electroencephalographic (EEG), and intracerebral EEG (icEEG) recording in presurgical evaluation. A Bluetooth and dual radio links were introduced for these recording. The Bluetooth-based device was embedded in a non-invasive multichannel EEG-NIRS system for easy portability and long term monitoring. On the other hand, the recorded icEEG signals from up to 128 channels through intracerebral electrodes are transmitted wirelessly to a remote base station. This transmitter front-end contains preamplifier, proper filtering, data converter and power amplifier. The RF back-end is based on commercial transceiver operated in the Medical Implant Communication Service (MICS) 400 MHz band. Power consumption of the front-end and transmitter are 0.75mW and 15mW, respectively. The proposed remote monitoring systems are validated in vitro using data recording from human patients.

A low-power implantable device for epileptic seizure detection and neurostimulation

In this paper, we present the design of a low-power closed-loop neurostimulator (CLNS) as an adjunctive treatment for patients with refractory partial epilepsy. The CLNS combines epileptic seizure detection with simultaneous electrical stimulation feedback. The system amplifies the neural signal with adjustable gain, detects epileptic low-voltage fast-activity, and generates programmable stimulation currents. The implemented seizure detector is based on a detection algorithm validated in Matlab tools and was tested using intracerebral electroencephalographic (iEEG) recordings from a patient with drug-resistant epilepsy. The amplifier, epileptic-seizure detector and electric stimulator were implemented using CMOS 0.18-μm process. The iEEG were assessed by the proposed CMOS building blocks and the predefined seizure suppression biphasic electrical stimulations were administrated at 2 to 3 sec after electrographical seizure onsets. The simulated power consumption of the CLNS has showed that the system could run on a button cell battery for more than 8 years.

Epileptic seizure onset detection prior to clinical manifestation

In this paper, we present the design of an epilepticseizure detector. This circuit is part of an implantable device used to continuously record intracerebral electroencephalographic signals through subdural and depth electrodes. The implemented seizure detector is based on a detection algorithm validated in Matlab tools and the circuits were implemented using CMOS 0.18-µm process. The proposed system was tested using intracerebral EEG recordings from two patients with drug-resistant epilepsy. Four seizures were assessed by the proposed CMOS building blocks and the required delays to detect these seizures were 3, 8, 11, and 11 sec, respectively after electric onset. The simulated total power consumption of the detector was 6.71 µW. Together, these preliminary results indicate the possibility of building implantable ultra-low power seizure-detection devices.

An Implantable Seizure-Onset Detector Based on a Dual-Path Single-Window Count-Based Technique for Closed-Loop Applications

In this paper, we present a single voltage-window count-based seizure onset detection algorithm and its associated hardware implementation. The proposed algorithm combines the advantages associated with voltage-window count-based and event-based threshold-voltage detections. The result is an algorithm that is more tolerant to noise, dc offsets, baseline energy variations, and seconds-long nonseizure related sharp activities. In addition, only one parameter (one threshold voltage) needs to be optimized per patient, and for that, only one seizure per patient is used for training, making the process of optimizing the patient-specific detector a simple task. The time evaluation period when counting is performed is kept constant across all patients studied, and is fixed at 5 s in this work. A novel dual path digital signal processing unit in the back-end of the detector is included and is shown to decrease the detection latency by 14%. Experimental results on a printed circuit board using commercially available discrete components confirm the correct functionality of the proposed detector. The proposed algorithm achieves 100% sensitivity, 10.7 s average detection delay, and a single false alarm when evaluated on a total of 25 seizures and 24 nonseizure datasets of intracerebral electroencephalographic (icEEG) recordings from five patients from the epilepsy monitoring unit of Notre-Dame Hospital in Montréal. In addition, monolithic integration of the overall system, including bio-amplification and comparison, is also carried out in a TSMC 0.18-