Key result
A self-assessment method utilizing machine learning and convolutional neural networks was developed to detect depression using speech acoustics, eye blink rate, and EEG signals.
Why the study?
Timely detection of depression is hindered by a severe scarcity of psychiatrists, creating a need for automated self-assessment methods.
Does a machine learning approach using speech acoustics, eye blink rate, and EEG signals detect depression in individuals?
Does a machine learning approach using speech acoustics, eye blink rate, and EEG signals detect depression in individuals?
A multimodal machine learning system using speech, eye blink, and EEG data was developed to aid in the self-assessment and detection of depression.
May facilitate earlier depression screening amid psychiatrist shortage in India; leaves open efficacy in prospective validation.
According to a population-based study in India, the prevalence of depression stands at 15.1%, affecting an estimated 57 million individuals in the country. This accounts for roughly 18% of the global population. However, a significant obstacle to timely detection of depression cases is the severe scarcity of psychiatrists, amounting to a staggering 77%. To address this issue, a self-assessment method has been developed, allowing individuals to assess whether they might be experiencing depression without the need for a psychiatrist's visit. This method can also be employed by psychiatrists to augment their diagnostic process and ensure prompt patient care. The self-assessment process involves a user interface that presents a series of questions. The responses are captured through speech acoustics, eye blink rate, and EEG signals. These data are then pre-processed and analysed individually using trained machine learning (ML) and convolutional neural network (CNN) models. The final outcome determines if an individual is experiencing depression. Additionally, the system generates a comprehensive report that includes acoustic spectrogram features, components, individual analyses, and outputs.
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Garg et al. (2023) studied Depression. Machine learning and CNN-based self-assessment method was evaluated on Detection of depression. A self-assessment method utilizing machine learning and convolutional neural networks was developed to detect depression using speech acoustics, eye blink rate, and EEG signals.
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