Synthesizing realistic 12-lead electrocardiogram (ECG) data is a complex task due to the intricate spatial and temporal dynamics of cardiac electrophysiology. Traditional generative models often struggle to capture the nuanced interdependencies among ECG leads, which are essential for accurate medical analysis. In this paper, we propose Physics-Inspired Partial Differential Equation GAN for Multilead ECG Synthesis (PhysioPDE-GAN), a generative framework designed to model the spatiotemporal structure of multilead ECG signals by incorporating physiological priors and spatial constraints directly into the generative process. By embedding PDE-based representations directly into the generative process, our approach effectively captures both the temporal evolution and spatial relationships between ECG leads. We conduct extensive experiments to evaluate the performance of various base classifiers trained on the synthetic 12-lead ECG data generated by PhysioPDE-GAN. These classifiers outperform those trained on data produced by other conventional methods, achieving statistically significant improvements in detecting cardiac abnormalities. Our work highlights the potential of combining PDE-driven cardiac models with advanced generative techniques to enhance the quality and utility of synthetic biomedical datasets.

Yakir Yehuda, Itzik Malkiel, Oren Barkan, Jonathan Weill, Royi Ronen, Noam Koenigstein. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these recorded conversations can be an arduous and time-intensive process, especially when dealing with long and multifaceted dialogues. In this work, we propose a novel method, which we name SegLLM, for efficient and accurate call segmentation and topic extraction. SegLLM is composed of offline and online phases. The offline phase is applied once to a given list of topics and involves generating a distribution of synthetic sentences for each topic using a large language model (LLM). The online phase is applied to every call separately and scores the similarity between the transcripted conversation and the topic anchors found in the offline phase. The proposed paradigm provides an accurate and efficient method for call segmentation and topic extraction that does not require labeled data, thus making it a versatile approach applicable to various domains.

Summarization techniques strive to create a concise summary that conveys the essential information from a given document. However, these techniques are often inadequate for summarizing longer documents containing multiple pages of semantically complex content with various topics. Hence, in this work, we present a Topic-Conditional Summarization (TCS) method, that produces different summaries each conforming to a different topic. TCS is an unsupervised method and does not require ground truth summaries. The proposed algorithm adapts the TextRank paradigm and enhances it with a language model specialized in a set of documents and their topics. Extensive evaluations across multiple datasets indicate that our method improves upon other alternatives by a sizeable margin.

Transcriptions of phone calls hold significant value in sales, customer service, healthcare, law enforcement, and more. However, analyzing recorded conversations can be a time-consuming process, especially for complex dialogues. In Microsoft Dynamics 365 Sales, a novel system, named GPT-Calls, is applied for efficient and accurate topic-based call segmentation. GPT-Calls comprises offline and online phases. In the offline phase, the system leverages a GPT model to generate synthetic sentences and extract anchor vectors for predefined topics. This phase, performed once on a given topic list, significantly reduces the computational burden. The online phase scores the similarity between the transcribed conversation and the topic anchors from the offline phase, followed by time domain analysis to group utterances into segments and tag them with topics. The GPT-Calls scheme offers an accurate and efficient approach to call segmentation and topic extraction, eliminating the need for labeled data. It is a versatile solution applicable to various industry domains. GPT-Calls operates in production under Dynamics 365 Sales Conversation Intelligence, applied to real sales conversations from diverse Dynamics 365 Sales tenants, streamlining call analysis, and saving time and resources while ensuring accuracy and effectiveness.

To improve the accuracy of clinical multivariate time series (MTS) classification (such as EEG and ECG) by a novel self-supervised paradigm that directly captures the dynamics between the different time series learned together to optimize the classification task. Labels in clinical datasets are very often insufficient. One way to address this challenge is leveraging self-supervision. This paradigm attempts to identify a supervisory signal inherent within a dataset to serve as a surrogate label. We present a novel form of self-supervision: dynamics of clinical MTS. Unlike other self-supervision methods, such as masking, that are intuitive but still heuristic, we suggest to learn a representation justified by Koopman theory. The latter was shown useful for representing clinical time series and can be used as a form of surrogate task to improve the clinical MTS classification. In the ECG task, we show that our proposed framework achieved higher sensitivity and specificity than the state-of-the-art (SOTA) baseline over numerous common diagnoses. For EEG abnormality classification, our proposed framework also achieved higher sensitivity and specificity than the SOTA baseline. All results are statistically significant. Our technique yields reliable clinical diagnosis in an empirical study employing signals from thousands of patients in multiple clinical tasks employing two types of clinical-grade sensors (ECG and EEG) as compared to the state-of-the-art machine learning. Leveraging time-series-dynamics self-supervision can help mitigate the lack of labels in clinical datasets used for training machine learning algorithms and significantly improve their performance. Specifically, the ECG system presented in this work is being trialed in hospitals, used by top cardiologists for patient diagnosis and treatment. We believe that the deployment of such cutting-edge technology will significantly improve the accuracy and speed of cardiac assessments.