Development of Computer-Aided Semi-Automatic Diagnosis System for Chronic Post-Stroke Aphasia Classification with Temporal and Parietal Lesions: A Pilot Study

Abstract

Survivors of either a hemorrhagic or ischemic stroke tend to acquire aphasia and experience spontaneous recovery during the first six months. Nevertheless, a considerable number of patients sustain aphasia and require speech and language therapy to overcome the difficulties. As a preliminary study, this article aims to distinguish aphasia caused from a temporoparietal lesion. Typically, temporal and parietal lesions cause Wernicke’s aphasia and Anomic aphasia. Differential diagnosis between Anomic and Wernicke’s has become controversial and subjective due to the close resemblance of Wernicke’s to Anomic aphasia when recovering. Hence, this article proposes a clinical diagnosis system that incorporates normal coupling between the acoustic frequencies of speech signals and the language ability of temporoparietal aphasias to delineate classification boundary lines. The proposed inspection system is a hybrid scheme consisting of automated components, such as confrontation naming, repetition, and a manual component, such as comprehension. The study was conducted involving 30 participants clinically diagnosed with temporoparietal aphasias after a stroke and 30 participants who had experienced a stroke without aphasia. The plausibility of accurate classification of Wernicke’s and Anomic aphasia was confirmed using the distinctive acoustic frequency profiles of selected controls. Accuracy of the proposed system and algorithm was confirmed by comparing the obtained diagnosis with the conventional manual diagnosis. Though this preliminary work distinguishes between Anomic and Wernicke’s aphasia, we can claim that the developed algorithm-based inspection model could be a worthwhile solution towards objective classification of other aphasia types.

Characteristics of Aphasia in Ischemic Stroke Patients at Dr. Mahar Mardjono National Brain Center Hospital Indonesia in 2021

“…The result of our study confirmed a study conducted by Silva et al (2020) that lesions in the temporal and parietal lobes are the underlying cause of anomic aphasia. Our research subjects who had anomic aphasia exhibited lesions in the temporal and parietal lobes.…”

Special Issue on Signal Processing and Machine Learning for Biomedical Data

“…In [4], the authors developed a system to classify Anomic and Wernicke's aphasia, based on the acoustic frequencies of speech signals. The system consisted of three diagnosis components-confrontation naming with 30 pictures to identify, single-word repetition of 15 single words, and comprehension components.…”

IoT-Based Solution for Detecting and Monitoring Upper Crossed Syndrome

“…Aside from wearables, some systems can be utilized to potentially detect UCS. According to Silva et al [ 28 ], a system was developed to determine and differentiate subtypes of aphasia that occur due to a stroke. This classification method utilizes acoustic frequencies from pathological speech production and a comprehension analysis to distinguish various aphasia types.…”