Science
The papers behind the products.
Twelve peer-reviewed papers, written with clinical and academic collaborators in Singapore, the United States, Canada, Belgium and Japan. Every entry links to the publisher record.
GenMind™
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Schizophrenia Research · 2025
The impact of smartphones and video games on real-time monitoring and schizophrenia treatment
Negative symptoms and cognitive impairment in schizophrenia differs from one situation to the next, showing up differently at home than in a clinic. Thus, continuous, real-world monitoring can identify changes that scheduled appointments can miss.
Ecological and momentary assessment and intervention for schizophrenia. With Johns Hopkins University, Kyoto University.
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Schizophrenia (Nature) · 2022
Using machine learning to identify schizophrenia and depression via objective linguistic, speech, facial, and motor behavioral cues
This study provides clear evidence that objective, behavioral signals leveraged by machine-learning models can help diagnose, assess, and monitor patients with schizophrenia and depression.
Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behaviour. With Nanyang Technological University, National University of Singapore.
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Springer Singapore · 2019
Using linguistic cues to identify how patients with schizophrenia speak, and what it reveals about their condition
This study explores the method of using a monitoring tool to pick up word-choice patterns passively from natural conversation, removing the need of a structured clinical setting.
Automated lexical analysis of interviews with individuals with schizophrenia. With Nanyang Technological University.
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IEEE EMBC · 2019
Analyzing voice and speech patterns in an automated objective pipeline to provide insight into symptoms of schizophrenia and depression
By providing an automated method to analyze symptoms of these two common mental disorders, it suggests that these kinds of tools can support ongoing tracking outside the clinical visit.
Automated verbal and non-verbal speech analysis of interviews of individuals with schizophrenia. With Nanyang Technological University.
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IEEE
Using automatic transcriptions of interviews to observe negative symptoms in schizophrenic patients
This automated method is a practical step towards scaling this type of assessment in a clinical setting. The study is also showcases the benefit of using lexical features in the understanding and diagnosis of schizophrenia.
With Nanyang Technological University.
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IEEE Cyberworlds · 2018
Using objective conversational cues in tracking negative symptoms in schizophrenia
Automated symptom analysis reduces the reliance on clinical availability, expanding where and how often assessments can be done.
Prediction of negative symptoms of schizophrenia from objective linguistic, acoustic and non-verbal cues. With Nanyang Technological University.
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NeuroBrowser™
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International Journal of Neural Systems · 2023
Proposing a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG)
The proposed seizure detector can detect seizures in adult EEGs in less than 15s for a 30min EEG. This can aid clinicians in reliably expediting seizure identification, providing more time for devising proper treatment.
Six-centre assessment of CNN-Transformer with belief matching loss for patient-independent seizure detection. With Nanyang Technological University, KU Leuven, McGill University.
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Journal of Neural Engineering · 2022
Analyzing epileptic EEGs without IEDs
These results are a jumping off point for automated detection of epilepsy. It is one of the first to analyze epileptic EEGs without IEDs, thus opening up an under-explored option in epilepsy diagnosis.
Improving automated diagnosis of epilepsy from EEGs beyond interictal epileptiform discharges. With Nanyang Technological University, University of Pennsylvania, McGill University.
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IEEE EMBC · 2022
Proposing an artificial detection module to reject artifact segments, leading to a cleaner EEG for further analysis
Artifacts cause EEG misinterpretation. The proposed system can reject a substantial amount of artifacts while only removing a small fraction of a clean EEG, improving readability of recordings.
Transformer convolutional neural networks for automated artifact detection in scalp EEG. With Nanyang Technological University.
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International Journal of Neural Systems · 2021
Evaluating features that may provide reliable IED detection and EEG classification
The proposed classification system in this study only takes a few seconds to analyze a 30-min routine EEG. This may help reduce the human effort required for epilepsy diagnosis.
Time-frequency decomposition of scalp electroencephalograms improves deep learning-based epilepsy diagnosis. With Nanyang Technological University, Stanford University, Massachusetts General Hospital.
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International Journal of Neural Systems · 2021
Proposing three automated approaches to detect slowing in EEG
These automated approaches achieved results comparable to human experts. Additionally, the deep-learning-based system can process a 30 minute EEG in 4 seconds, and can be deployed to assist clinicians in interpreting EEGs.
Multi-centre validation of automated classification of pathological slowing in adult scalp EEG. With Nanyang Technological University.
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International Journal of Neural Systems · 2021
Classifying EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG
The six-center study shows strong generalization of effectiveness. The system can also process routine EEGs in seconds, effectively aiding clinicians in diagnosing epilepsy.
Automated adult epilepsy diagnostic tool based on interictal scalp EEG characteristics, a six-centre study. With Nanyang Technological University, Stanford University, Massachusetts General Hospital.
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Awards and certifications
Recognition along the way.
- MedTech Innovator, Asia Pacific 2019
- AI NY Summit 2022, Runner Up
- Mayo Clinic and ASU MedTech Accelerator, 2024
- AMII 2025 test bedding with the National Neuroscience Institute
- ISO 13485 and 13485EN certified
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