False arrhythmia alarms in intensive care units (ICUs) are a continuing problem despite considerable effort from industrial and academic algorithm developers. Of all life-threatening arrhythmias, ventricular tachycardia (VT) stands out as the most challenging arrhythmia to detect reliably. We introduce a new annotated VT alarm database, VTaC (Ventricular Tachycardia annotated alarms from ICUs) consisting of over 5,000 waveform recordings with VT alarms triggered by bedside monitors in the ICU. Each VT alarm waveform in the dataset has been labeled by at least two independent human expert annotators. The dataset encompasses data collected from ICUs in two major US hospitals and includes data from three leading bedside monitor manufacturers, providing a diverse and representative collection of alarm waveform data. Each waveform recording comprises at least two electrocardiogram (ECG) leads and one or more pulsatile waveforms, such as photoplethysmogram (PPG or PLETH) and arterial blood pressure (ABP) waveforms. We demonstrate the utility of this new benchmark dataset for the task of false arrhythmia alarm reduction, and present performance of multiple machine learning approaches, including conventional supervised machine learning, deep learning, semi-supervised learning, and generative approaches for the task of VT false alarm reduction.

Human speech often contains ambiguity stemming from the use of demonstrative pronouns (DPs), such as “this” and “these.” While we can typically decipher which objects of interest DPs are referring to based on context, modern day voice assistants (VAs – such as Google Assistant and Siri) are yet unable to process queries containing such ambiguity. For instance, to humans, a question such as “how much is this?” can be clarified through visual reference (e.g., a buyer gestures to the seller the object they would like to purchase). To bridge this gap between human and machine cognition, we built and examined a touch + voice multimodal VA prototype that enables users to select key spatial information to embed as context and query the VA. The prototype converts results of mobile, real-time object recognition and optical character recognition models into augmented reality buttons that represent features. Users can interact with and modify the selected features through a word grid. We conducted a study to investigate: 1) how touch performs as an additional modality to resolve ambiguity in queries, 2) how users use DPs when interacting with VAs, and 3) how users perceive a VA that can understand DPs. From this procedure we found that as the query becomes more complex, users prefer the multimodal VA over the standard VA without experiencing elevated cognitive load. Additionally, even though it took some time getting used to, many participants eventually became comfortable with using DPs to interact with the multimodal VA and appreciated the improved human-likeness of human-VA conversations.