Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors.

Abstract

Background: Stroke is the second leading cause of death worldwide. Cerebrovascular diseases (CVDs), including strokes and transient ischemic attacks (TIAs), cause long-term disabilities and economic burdens. Strokes can be ischemic, caused by blood clots; or hemorrhagic, caused by bleeding in the brain. Immediate diagnosis and treatment are crucial. Proper management of TIAs is essential due to the high risk of subsequent strokes. Currently, wearable sensors, AI-based prediction, and automatic video analysis are not used in CVD diagnosis and recovery estimation.

Objective: This paper describes the design, cohort characteristics, and multimodal dataset of the Stroke-Data study conducted at 2 university hospitals in Finland. Although findings from individual components of the study have been reported elsewhere, this paper provides the first comprehensive description of the overall study design, cohort, and multimodal data collection protocol. The study collected a unique multimodal dataset comprising sensor data, video recordings, clinical assessments, questionnaires, and health record data from healthy controls and patients with stroke and TIA to support the development of AI, sensor-based, and video-based methods for CVD diagnosis and recovery estimation.

Methods: Stroke-Data was a prospective national multicenter study conducted at Oulu University Hospital and Kuopio University Hospital from October 2021 to December 2022. A rich multimodal dataset from patients with stroke and TIA and healthy controls was collected by study nurses and a research assistant. Inclusion criteria for patients were age >18 years and a diagnosis of cerebrovascular accident or TIA within the previous 3 days. Healthy control participants were aged 18 years and older and had no major chronic diseases, although blood pressure and cholesterol medications were permitted. Data collection included electroencephalogram; electrocardiogram; near-infrared spectroscopy; accelerometers to analyze gait and balance; retinal fundus images; video recordings of neurological examinations to analyze the face, body movements, and speech; clinical assessments; questionnaires; and electronic health records.

Results: In total, 263 participants were recruited, of whom 123 were controls (median age 59, IQR 45-72 years; female: 84/123, 68.3%), 31 were patients with TIA (median age 71, IQR 60-80 years; female: 8/31, 25.8%), and 103 were patients with stroke (median age 70, IQR 59-76 years; female: 42/102, 41.2%, sex data missing from 1 participant). A total of 6 participants were excluded (5 had diagnoses other than TIA or stroke and 1 control participant had a previous CVD).

Conclusions: We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and TIA and healthy controls in a multicenter setting.

International registered report identifier (irrid): DERR1-10.2196/86930.