Muhammad Awais Bin Altaf

Scopus Publications

Biomarker Discovery for Autism Prediction Using Massive Feature Extraction Based on EEG Signals
Nauman Hafeez, Abdul Rehman Aslam, Muhammad Awais Bin Altaf
Sensors, 2026
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that requires early diagnosis for better intervention. However, current clinical behavioural examinations are time-consuming and prone to human error. Objective and effective biomarkers are essential for the diagnosis and prognosis of the disorder. Electroencephalography (EEG) is a non-invasive and inexpensive brain-imaging technique that is widely applied in the diagnosis of ASD. Feature-based methods have shown better performance in EEG-based applications. Here, we present a prediction framework based on massive feature extraction using the highly comparative time-series analysis (HCTSA) method and a hybrid feature selection method for the classification of ASD from resting-state EEG. Machine-learning models are trained and tested on a different number of selected features. Our models demonstrated 100% accuracy with ≥50 features on a balanced dataset of 56 participants. The most discriminating EEG channels and features were used in the prediction process, as well as those using Shapley values to provide explainability of our framework. Whilst these results are promising, we acknowledge the limitations of a single small-scale dataset and emphasise the need for validation on larger independent cohorts before clinical translation.
Simplified eye sensitivity model based fast tone mapping algorithm for HDR images
Naureen Mujtaba, Ishtiaq Rasool Khan, Nadeem Ahmad Khan, Muhammad Awais Bin Altaf
Signal Image and Video Processing, 2025
A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection †
Muhammad Sheeraz, Abdul Rehman Aslam, Emmanuel Mic Drakakis, Hadi Heidari, Muhammad Awais Bin Altaf, Wala Saadeh
Sensors, 2024
Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.
Epileptic Seizure Detection and Prediction for Patient Support
Gul Hameed Khan, Nadeem Ahmad Khan, Wala Saadeh, Muahammad Awais Bin Altaf
Communications in Computer and Information Science, 2024
A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals
Gul Hameed Khan, Nadeem Ahmad Khan, Muhammad Awais Bin Altaf, Qammer Abbasi
Sensors, 2023
This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children’s Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.
A Wearable EEG Acquisition Device With Flexible Silver Ink Screen Printed Dry Sensors
Muhammad Sheeraz, Wala Saadeh, Muhammad Awais Bin Altaf
Proceedings IEEE International Symposium on Circuits and Systems, 2023
Current electroencephalogram (EEG) measuring systems are bulky, impose constraints on patients, and require pre and post-measuring procedures. Usually, the EEG systems use either wet or dry EEG sensors, with the former suffers from skin preparation, the issues of adhesive conductive gels, and one-time usability whereas the latter causes skin irritation, abrasion, and pain upon pressure. Hence, these sensors are not suitable for long-term measurements. This paper presents a novel, wireless, behind-the-ear wearable EEG acquisition device that incorporates flexible dry EEG sensors. Silver ink-printed flexible sensors are fabricated using screen printing to overcome the above-mentioned drawbacks and limitations of conventional EEG sensors. The flexible sensors form a capacitive link with the skin via an adhesive layer between the sensor and the person's skin and are capable of acquiring the EEG without any skin preparation or gel. The performance of the printed flexible EEG sensors is tested by comparing them with the standard Ag/AgCl pre-gelled sensors. The alpha wave test and evoked potential EEG test are also performed for verification. The proposed device has a small form factor similar to a hearing aid and an in-house configurable Analog Front End (AFE) and Digital Back End (DBE) Processor and is capable of acquiring continuous EEG for a longer duration in a user-friendly and socially discrete manner.
Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification
Abdul Rehman Aslam, Nauman Hafeez, Hadi Heidari, Muhammad Awais Bin Altaf
Frontiers in Neuroscience, 2022
Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.
Multiphysiological Shallow Neural Network-Based Mental Stress Detection System for Wearable Environment
Muhammad Sheeraz, Abdul Rehman Aslam, Muhammad Awais Bin Altaf
Proceedings IEEE International Symposium on Circuits and Systems, 2022
Health problems related to stress are increasing globally and significantly affect the mental health and quality of life of human beings. Continuous suffering from stress may lead to serious psychological and physical health problems. But still, no effective and reliable stress detection methods are available. In this paper, a novel wearable device is presented to measure electroencephalogram (EEG) and electrocardiogram (ECG) simultaneously in a non-invasive approach. This system includes an analog front end (AFE) integrated with a machine learning-based digital backend (DBE) processor for mental stress prediction using only 3 electrodes. A PCB prototype is developed using the commercial off-the-shelf components. The developed prototype shows excellent noise performance of $0.1\\mu V_{rms}$ and predicts the mental stress with a classification accuracy of 92.7%. The proposed system is lightweight and easily wearable (behind the ear). The data is acquired from 25 participants for different stress scenarios including the Arithmetic Test and Stroop Color Word Test. Different EEG and ECG based features combinations are used for the classification of stress conditions using a shallow neural network (SNN) classifier.
Shallow Sparse Autoencoder Based Epileptic Seizure Prediction
Gul Hameed Khan, Nadeem Ahmad Khan, Muhammad Awais Bin Altaf
Proceedings 2022 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2022, 2022
Epileptic patients ’ quality of life can be significantly improved by epileptic seizure prediction based on scalp electroencephalogram (EEG). With the advancement of brain e-health technologies, there is an essential need for a method that accurately predicts seizures while running on computing platforms with very low computing resources. Moreover, existing methods do not provide EEG analysis on an individual channel basis to identify the abnormalities in the data. In order to address this issue, we propose an efficient framework for patient-specific seizure prediction. A hybrid model comprising of a shallow autoencoder (AE) with only one hidden layer and a support vector machine (SVM) classifier has been developed. Both multi-channel and single channel EEG signal processing schemes have been developed. Generating a lower dimensional sparse signal with AE in the first stage and classifying the signal using SVM in the second stage are the two stages that the model separates into when processing EEG data. We initially train the AE to provide an optimum sparse signal and then use this sparse signal as input for an SVM classifier to categorize the EEG data. Using the 10-fold cross validation strategy, the proposed model tests 13 patients from the CHB-MIT dataset and achieves an average sensitivity of 98% and an average area under the curve (AUC) of 99%. We have compared our hybrid approach ’s performance with both deep learning models and traditional techniques. The proposed methodology outperforms state of the art seizure prediction methods, demonstrating its effectiveness.
A 2.7μJ/classification Machine-Learning based Approximate Computing Seizure Detection SoC
Abdul Muneeb, Mubashir Ali, Muhammad Awais Bin Altaf
Proceedings IEEE International Symposium on Circuits and Systems, 2022
An electroencephalogram (EEG) based non-invasive 2-channel System on Chip (SoC) is presented to detect and report the seizure event of the epileptic patient. The SoC incorporates an area and power-efficient dual-channel analog front-end (AFE) and machine learning-based differential difference approximate computing seizure detection ($\\text{D}^{2}$ACSD) processor. The $\\text{D}^{2}$ACSD processor integrates approximate computing feature extraction and fixed-point linear support vector machine (LSVM) classifier to minimize the area-and-power utilization. The AFE comprises of two duty-cycled resistive MOSFET (DCRM) capacitively coupled instrumentation amplifier ($\\text{C}^{2}$IA), a programmable gain amplifier, and multiplexed SAR-ADC. The DCRM-C2IA utilizes proposed DCRM technique to boost the equivalent resistance of the integrator of the DC servo loop. The 5m$\\text{m}^{2}$SoC is implemented in 0.18$\\mu$m, CMOS process while achieving an average accuracy of 89.19%, sensitivity 92.18% and specificity 89.13% for the random and block-wise splitting of data in train/test sets. The implemented DCRM-C2IA achieves an integrated noise of 0.80$\\mu$Vrms over 0.5-100Hz frequency band. The realized system consumes $2.7\\mu \\text{J}/$classification to continuously detect seizure onset for timely suppression.
A 73 μW single channel Photoplethysmography-based Blood Pressure Estimation Processor
Abdul Rehman, Muhammad Awais Bin Altaf, Wala Saadeh
Proceedings IEEE International Symposium on Circuits and Systems, 2022
A Fast HDR Image TMO based on a Simplified Eye Sensitivity Model
Naureen Mujtaba, Ishtiaq Rasool Khan, Nadeem Ahmad Khan, Muhammad Awais Bin Altaf
IEEE Workshop on Signal Processing Systems Sips Design and Implementation, 2022
A Closed-Loop Ear Wearable EEG Measurement Device with Realtime Electrode Skin Impedance Measurement
Muhammad Sheeraz, Abdul Rehman Aslam, Muhammad Awais Bin Altaf, Hadi Heidari
20th IEEE International Interregional Newcas Conference Newcas 2022 Proceedings, 2022
A Wearable High Blood Pressure Classification Processor Using Photoplethysmogram Signals through Power Spectral Density Features
Muhammad Sheeraz, Abdul Rehman Aslam, Nauman Hafeez, Hadi Heidari, Muhammad Awais Bin Altaf
Proceeding IEEE International Conference on Artificial Intelligence Circuits and Systems Aicas 2022, 2022
A 10.13μJ/Classification 2-Channel Deep Neural Network Based SoC for Negative Emotion Outburst Detection of Autistic Children
Abdul Rehman Aslam, Muhammad Awais Bin Altaf
IEEE Transactions on Biomedical Circuits and Systems, 2021
An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network
Abdul Rehman Aslam, Nauman Hafeez, Hadi Heidari, Muhammad Awais Bin Altaf
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems Aicas 2021, 2021
A Low Power Multi-Class Migraine Detection Processor Based on Somatosensory Evoked Potentials
Zain Taufique, Bingzhao Zhu, Gianluca Coppola, Mahsa Shoaran, Muhammad Awais Bin Altaf
IEEE Transactions on Circuits and Systems II Express Briefs, 2021
Machine learning-based patient-specific processor for the early intervention in autistic children through emotion detection
Abdul Rehman Aslam, Muhammad Awais Bin Altaf
Neural Engineering Techniques for Autism Spectrum Disorder Volume 1 Imaging and Signal Analysis, 2021
Wearable Low-power Closed-loop System for Tremor Detection and Stimulation using Electromyography (EMG)
Muhammad Rizwan Khan, Wala Saadeh, Muhammad Awais Bin Altaf
2021 28th IEEE International Conference on Electronics Circuits and Systems Icecs 2021 Proceedings, 2021
Approximate Feature Extraction for Low Power Epileptic Seizure Prediction in Wearable Devices
Zain Taufique, Anil Kanduri, Muhammad Awais Bin Altaf, Pasi Liljeberg
2021 IEEE Nordic Circuits and Systems Conference Norcas 2021 Proceedings, 2021
An 8.7 μj/class. FFT accelerator and DNN-based configurable SoC for Multi-Class Chronic Neurological Disorder Detection
Zain Taufique, Bingzhao Zhu, Gianluca Coppola, Mahsa Shoaran, Wala Saadeh, Muhammad Awais Bin Altaf
Proceedings A Sscc 2021 IEEE Asian Solid State Circuits Conference, 2021
EDM: A multiclassification support system to identify seizure type using K Nearest Neighbor
Shiza Shakeel, Niha Afzal, Gul Hameed Khan, Nadeem Ahmad Khan, Mujeeb ur Rehman Abid, Muhammad Awais Bin Altaf
2021 28th IEEE International Conference on Electronics Circuits and Systems Icecs 2021 Proceedings, 2021
An On-Chip Processor for Chronic Neurological Disorders Assistance Using Negative Affectivity Classification
Abdul Rehman Aslam, Muhammad Awais Bin Altaf
IEEE Transactions on Biomedical Circuits and Systems, 2020
Design of energy-efficient electrocorticography recording system for intractable epilepsy in implantable environments
Proceedings IEEE International Symposium on Circuits and Systems, 2020
An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System
Hafiz Talha Iqbal, Bilal Majeed, Uzair Khan, Muhammad Awais Bin Altaf
Biocas 2019 Biomedical Circuits and Systems Conference Proceedings, 2019
Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia
Wala Saadeh, Fatima Hameed Khan, Muhammad Awais Bin Altaf
IEEE Transactions on Biomedical Circuits and Systems, 2019
Wearable ECG Measurement System for Detection of Cardiac Arrhythmia
Arish Adil, Hassan Abid, Nadir Najib, Usama Jillani, Wala Saadeh, Muhammad Awais Bin Altaf
2019 Uk China Emerging Technologies Ucet 2019, 2019
A Patient-specific single sensor iot-based wearable fall prediction and detection system
Wala Saadeh, Saad Adnan Butt, Muhammad Awais Bin Altaf
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019
A Wearable Neuro-Degenerative Diseases Classification System Using Human Gait Dynamics
Wala Saadeh, Muhammad Awais Bin Altaf
IFIP Advances in Information and Communication Technology, 2019
An 8 channel patient specific neuromorphic processor for the early screening of autistic children through emotion detection
Abdul Rehman Aslam, Muhammad Awais Bin Altaf
Proceedings IEEE International Symposium on Circuits and Systems, 2019
A Wearable Auto-Patient Adaptive ECG Processor for Shockable Cardiac Arrhythmia
Syed Muhammad Abubakar, Muhammad Rizwan Khan, Wala Saadeh, Muhammad Awais Bin Altaf
2018 IEEE Asian Solid State Circuits Conference A Sscc 2018 Proceedings, 2018
A wearable neuro-degenerative diseases detection system based on gait dynamics
Wala Saadeh, Muhammad Awais Bin Altaf, Saad Adnan Butt
IEEE IFIP International Conference on VLSI and System on Chip VLSI Soc, 2017
A 1.1-mW Ground Effect-Resilient Body-Coupled Communication Transceiver with Pseudo OFDM for Head and Body Area Network
Wala Saadeh, Muhammad Awais Bin Altaf, Haneen Alsuradi, Jerald Yoo
IEEE Journal of Solid State Circuits, 2017
A 0.21 μj patient-specific REM/Non-REM sleep classifier for Alzheimer patients
Muhammad Awais Bin Altaf, Wala Saadeh
2017 IEEE Biomedical Circuits and Systems Conference Biocas 2017 Proceedings, 2017
A low complexity patient-specific threshold based accelerator for the Grand-mal seizure disorder
Muhammad Rizwan Khan, Wala Saadeh, Muhammad Awais Bin Altaf
2017 IEEE Biomedical Circuits and Systems Conference Biocas 2017 Proceedings, 2017
Towards Design and Automation of Hardware-Friendly NOMA Receiver with Iterative Multi-User Detection
Muhammad Adeel Pasha, Momin Uppal, Muhammad Hassan Ahmed, Muhammad Aimal Rehman, Muhammad Awais Bin Altaf
Proceedings Design Automation Conference, 2017
A high accuracy and low latency patient-specific wearable fall detection system
Wala Saadeh, Muhammad Awais Bin Altaf, Muhammad Shoaib Bin Altaf
2017 IEEE EMBS International Conference on Biomedical and Health Informatics Bhi 2017, 2017
A Pseudo OFDM with Miniaturized FSK Demodulation Body-Coupled Communication Transceiver for Binaural Hearing AIDS in 65 nm CMOS
Wala Saadeh, Muhammad Awais Bin Altaf, Haneen Alsuradi, Jerald Yoo
IEEE Journal of Solid State Circuits, 2017
A 1.1mW hybrid OFDM ground effect-resilient body coupled communication transceiver for head and body area network
Wala Saadeh, Haneen Alsuradi, Muhammad Awais Bin Altaf, Jerald Yoo
2016 IEEE Asian Solid State Circuits Conference A Sscc 2016 Proceedings, 2017
Design of energy-efficient on-chip EEG classification and recording processors for wearable environments
Muhammad Awais Bin Altaf, Chen Zhang, Ljubomir Radakovic, Jerald Yoo
Proceedings IEEE International Symposium on Circuits and Systems, 2016
Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System
Chen Zhang, Muhammad Awais Bin Altaf, Jerald Yoo
IEEE Journal of Biomedical and Health Informatics, 2016
A 1.83 μj/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine
Muhammad Awais Bin Altaf, Jerald Yoo
IEEE Transactions on Biomedical Circuits and Systems, 2016
A 2.45μW patient-specific non-invasive transcranial electrical stimulator with an adaptive skin-electrode impedance monitor
Muhammad Awais Bin Altaf, Jerald Yoo
IEEE Biomedical Circuits and Systems Conference Engineering for Healthy Minds and Able Bodies Biocas 2015 Proceedings, 2015
A 16-channel, 1-second latency patient-specific seizure onset and termination detection processor with dual detector architecture and digital hysteresis
Chen Zhang, Muhammad Awais Bin Altaf, Jerald Yoo
Proceedings of the Custom Integrated Circuits Conference, 2015
A 16-Channel Patient-Specific Seizure Onset and Termination Detection SoC with Impedance-Adaptive Transcranial Electrical Stimulator
Muhammad Awais Bin Altaf, Chen Zhang, Jerald Yoo
IEEE Journal of Solid State Circuits, 2015
A 16-ch patient-specific seizure onset and termination detection SoC with machine-learning and voltage-mode transcranial stimulation
Muhammad Awais Bin Altaf, Chen Zhang, Jerald Yoo
Digest of Technical Papers IEEE International Solid State Circuits Conference, 2015
A 1.52 uJ/classification patient-specific seizure classification processor using Linear SVM
Muhammad Awais Bin Altaf, Jerald Yoo
Proceedings IEEE International Symposium on Circuits and Systems, 2013
A 1.83μJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC
M. A. B. Altaf, J. Tillak, Y. Kifle, J. Yoo
Digest of Technical Papers IEEE International Solid State Circuits Conference, 2013
An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor
Jerald Yoo, Long Yan, Dina El-Damak, Muhammad Awais Bin Altaf, Ali H. Shoeb, Anantha P. Chandrakasan
IEEE Journal of Solid State Circuits, 2013
An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor
Jerald Yoo, Long Yan, Dina El-Damak, Muhammad Bin Altaf, Ali Shoeb, Hoi-Jun Yoo, Anantha Chandrakasan
Digest of Technical Papers IEEE International Solid State Circuits Conference, 2012
A new approach to the implementation of VoIP for SOHO network
Ali Mohsin, Muhammad Awais Bin Altaf, Noor M. Sheikh, Muhammad Awais Javed, Muhammad Usman
2009 3rd International Conference on Electrical Engineering Icee 2009, 2009

MOST CITED SCHOLAR PUBLICATIONS

An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor
J Yoo, L Yan, D El-Damak, MAB Altaf, AH Shoeb, AP Chandrakasan
IEEE journal of solid-state circuits 48 (1), 214-228 , 2012
2012
Citations: 359
A 16-channel patient-specific seizure onset and termination detection SoC with impedance-adaptive transcranial electrical stimulator
MAB Altaf, C Zhang, J Yoo
IEEE Journal of Solid-State Circuits 50 (11), 2728-2740 , 2015
2015
Citations: 216
A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System
W Saadeh, SA Butt, MAB Altaf
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (5 … , 2019
2019
Citations: 196
A 1.83 uJ/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine
MAB Altaf, J Yoo
IEEE Transactions on Biomedical Circuits and Systems 10 (1), 49-60 , 2016
2016
Citations: 150
A 1.1-mW ground effect-resilient body-coupled communication transceiver with pseudo OFDM for head and body area network
W Saadeh, MAB Altaf, H Alsuradi, J Yoo
IEEE Journal of Solid-State Circuits 52 (10), 2690-2702 , 2017
2017
Citations: 100
An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor
J Yoo, L Yan, D El-Damak, MAB Altaf, A Shoeb, HJ Yoo, A Chandrakasan
2012 IEEE International Solid-State Circuits Conference, 292-294 , 2012
2012
Citations: 88
Design and implementation of a machine learning based EEG processor for accurate estimation of depth of anesthesia
W Saadeh, FH Khan, MAB Altaf
IEEE transactions on biomedical circuits and systems 13 (4), 658-669 , 2019
2019
Citations: 86
A pseudo OFDM with miniaturized FSK demodulation body-coupled communication transceiver for binaural hearing aids in 65 nm CMOS
W Saadeh, MAB Altaf, H Alsuradi, J Yoo
IEEE Journal of Solid-State Circuits 52 (3), 757-768 , 2017
2017
Citations: 64
An on-chip processor for chronic neurological disorders assistance using negative affectivity classification
AR Aslam, MAB Altaf
IEEE Transactions on Biomedical Circuits and Systems 14 (4), 838-851 , 2020
2020
Citations: 63
A 1.83 µJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC
MAB Altaf, J Tillak, Y Kifle, J Yoo
2013 IEEE International Solid-State Circuits Conference Digest of Technical … , 2013
2013
Citations: 58
Design and implementation of an on-chip patient-specific closed-loop seizure onset and termination detection system
C Zhang, MAB Altaf, J Yoo
IEEE journal of biomedical and health informatics 20 (4), 996-1007 , 2016
2016
Citations: 56
A Wearable Long-Term Single-Lead ECG Processor for Early Detection of Cardiac Arrhythmia
SM Abubakar, W Saadeh, MAB Altaf
IEEE/ACM Design, Automation and Test in Europe (DATE), 961-966 , 2018
2018
Citations: 52
A10. 13uJ/classification 2-channel deep neural network-based SoC for emotion detection of autistic children
AR Aslam, T Iqbal, M Aftab, W Saadeh, MAB Altaf
2020 IEEE Custom Integrated Circuits Conference (CICC), 1-4 , 2020
2020
Citations: 48
A high accuracy and low latency patient-specific wearable fall detection system
W Saadeh, MAB Altaf, MSB Altaf
2017 IEEE EMBS International Conference on Biomedical & Health Informatics … , 2017
2017
Citations: 39
An 8 channel patient specific neuromorphic processor for the early screening of autistic children through emotion detection
AR Aslam, MAB Altaf
2019 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5 , 2019
2019
Citations: 37
A shallow autoencoder framework for epileptic seizure detection in EEG signals
GH Khan, NA Khan, MAB Altaf, Q Abbasi
Sensors 23 (8), 4112 , 2023
2023
Citations: 36
A wearable auto-patient adaptive ECG processor for shockable cardiac arrhythmia
SM Abubakar, MR Khan, W Saadeh, MAB Altaf
2018 IEEE Asian Solid-State Circuits Conference (A-SSCC), 267-268 , 2018
2018
Citations: 35
21.8 A 16-ch patient-specific seizure onset and termination detection SoC with machine-learning and voltage-mode transcranial stimulation
MAB Altaf, C Zhang, J Yoo
2015 IEEE International Solid-State Circuits Conference-(ISSCC) Digest of … , 2015
2015
Citations: 35
A 10.13 uJ/classification 2-channel Deep Neural Network based SoC for Negative Emotion Outburst Detection of Autistic Children
AR Aslam, MAB Altaf
IEEE Transactions on Biomedical Circuits and Systems 15 (5), 1039-1052 , 2021
2021
Citations: 34
An ECG processor for the detection of eight cardiac arrhythmias with minimum false alarms
MA Sohail, Z Taufique, SM Abubakar, W Saadeh, MAB Altaf
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 1-4 , 2019
2019
Citations: 33

Muhammad Awais Bin Altaf

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS