Instant Preliminary Cardiac Analysis from Smartphone Auscultation: A Real-World Canine Heart Sound Dataset and Evaluation

TL;DR

This paper introduces a new canine heart sound dataset and evaluates a smartphone-based machine learning algorithm for preliminary cardiac analysis, demonstrating high accuracy in real-world veterinary telehealth scenarios.

Contribution

It provides a novel real-world canine heart sound dataset and assesses a new multi-stage fallback algorithm for reliable smartphone-based cardiac analysis.

Findings

Achieved over 91% heart rate accuracy with the primary model.

Demonstrated feasibility of smartphone recordings for clinical cardiac assessment.

Validated the approach across diverse real-world data from multiple continents.

Abstract

This study presents a real-world canine heart sound dataset and evaluates SoNUS version 3.2.x, a machine learning algorithm for preliminary cardiac analysis using smartphone microphone recordings. More than one hundred recordings were collected from dogs across four continents, with thirty eight recordings annotated by board certified veterinary cardiologists for quantitative evaluation. SoNUS version 3.2.x employs a multi-stage fallback architecture with quality-aware filtering to ensure reliable output under variable recording conditions. The primary sixty second model achieved mean and median heart rate accuracies of ninety one point six three percent and ninety four point nine five percent, while a fast model optimized for thirty to forty second recordings achieved mean and median accuracies of eighty eight point eight six percent and ninety two point nine eight percent. These results demonstrate the feasibility of extracting clinically relevant cardiac information from opportunistic smartphone recordings, supporting scalable preliminary assessment and telehealth applications in veterinary cardiology.