An efficient embedded hardware for high accuracy detection of epileptic seizures

Saleheen, Mushfiq U.; Alemzadeh, Homa; Cheriyan, Ajay M.; Kalbarczyk, Zbigniew; Iyer, Ravishankar K.

doi:10.1109/bmei.2010.5639541

“…We refer the reader to [10] for a comprehensive survey of epileptic seizure detection and prediction systems. While Artificial Neural Networks (ANNs) have previously been used for epileptic seizure detection [11] and prediction [12] on FPGA, no previous work has investigated the use of memristors for the detection or prediction of epileptic seizures using DL, which could drastically improve the performance on the IoMT edge. Fig.…”

Section: Related Workmentioning

confidence: 99%

Towards Memristive Deep Learning Systems for Real-Time Mobile Epileptic Seizure Prediction

Lammie

¹

,

Wang

²

,

Azghadi

³

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform real-time epileptic seizure prediction on the edge. Using the MemTorch simulation framework and the Children's Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) dataset we determine the performance of various simulated MDLS configurations. An average sensitivity of 77.4% and a Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.85 are reported for the optimal configuration that can process Electroencephalogram (EEG) spectrograms with 7,680 samples in 1.408ms while consuming 0.0133W and occupying an area of 0.1269mm 2 in a 65nm Complementary Metal-Oxide-Semiconductor (CMOS) process.

show abstract

“…Artificial Neural Networks (ANNs) have previously been used for epileptic seizure detection [11] and prediction [12] on FPGA, no previous work has investigated the use of memristors for the detection or prediction of epileptic seizures using DL, which could drastically improve the performance on the IoMT edge.…”

Section: Related Workmentioning

confidence: 99%

Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction

Lammie,

Xiang,

Azghadi

2021

Preprint

0

View full text Add to dashboard Cite

The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform real-time epileptic seizure prediction on the edge. Using the MemTorch simulation framework and the Children's Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) dataset we determine the performance of various simulated MDLS configurations. An average sensitivity of 77.4% and a Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.85 are reported for the optimal configuration that can process Electroencephalogram (EEG) spectrograms with 7,680 samples in 1.408ms while consuming 0.0133W and occupying an area of 0.1269mm 2 in a 65nm Complementary Metal-Oxide-Semiconductor (CMOS) process.

show abstract

“…Examples demonstrated by previous works include: (i) a combination of the spectral and correlation analysis blocks can be used to compute a coherence metric in detecting cognitive decline [10], (ii) a seizure detection scheme can be implemented by performing local variance computation using the mean analysis block [11], and (iii) as part of ECG analysis, combining QRS and correlation analysis blocks can detect irregularity in heart beats [12]. This flexibility in feature selection is provided by the Feature Selection and Aggregation block.…”

Section: Architectural Overviewmentioning

confidence: 99%

“…For EEG-based epileptic seizure detection, we use a technique based on variance analysis presented in [11]. In this work, the sample entropy and variancebased feature extraction techniques are evaluated in terms of accuracy and hardware implementation overhead.…”

Section: A Epileptic Seizure Detectionmentioning

confidence: 99%

See 1 more Smart Citation

RMED: A reconfigurable architecture for embedded medical monitoring

Alemzadeh

¹

,

Saleheen

²

,

Jin

³

et al. 2011

2011 IEEE/NIH Life Science Systems and Applications Workshop (LiSSA)

Self Cite

4

0

View full text Add to dashboard Cite

In this paper we propose a multi-parameter reconfigurable architecture framework for patient-specific medical monitoring. This architecture is mainly composed of a set of heterogeneous processing engines and flexible communication interfaces, which enable the run-time configuration of the architecture for optimal diagnosis of different diseases. The flexibility of the proposed framework is evaluated by demonstrating two different medical applications for monitoring brain and heart status on an FPGA-based hardware prototype. The evaluated epileptic seizure detection application gains a high detection performance with overall accuracy of 98.52% and sensitivity of 99.47%. For the cardiac ICU monitoring application, the experimental results for detecting abnormality of blood pressure and heart rate in selected patients show a high true positive rate of 94.74%. By applying algorithmic enhancements in the detection scheme, we even achieve early detection of abnormalities in blood pressure in the range of few minutes before standard ICU monitor alarms with a true positive rate of 64%. With a balanced mixture of flexibility, patient-specificity, and detection accuracy at small hardware footprint, the proposed architecture can be an attractive framework for embedded monitoring of a wide variety of medical conditions.

show abstract

An efficient embedded hardware for high accuracy detection of epileptic seizures

Cited by 14 publications

References 16 publications

Towards Memristive Deep Learning Systems for Real-Time Mobile Epileptic Seizure Prediction

Towards Memristive Deep Learning Systems for Real-Time Mobile Epileptic Seizure Prediction

Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction

RMED: A reconfigurable architecture for embedded medical monitoring

Contact Info

Product

Resources

About