The medical electronic stethoscope, through multi-channel synchronous acquisition technology, upgrades traditional auscultation from single-source sound capture to multi-dimensional physiological signal integration, providing crucial technical support for the accurate diagnosis of complex cardiac diseases. The core of this technology lies in the spatiotemporal synchronous acquisition of acoustic signals from various parts of the heart through the collaborative work of multiple sensors. Combined with digital signal processing and artificial intelligence algorithms, it overcomes the limitations of traditional auscultation in terms of spatial resolution and signal integrity.
The realization of multi-channel synchronous acquisition first relies on precise hardware design. The medical electronic stethoscope typically employs a distributed sensor array, arranging multiple high-sensitivity pickup units in a specific topology at key auscultation sites such as the left sternal border, the apex of the heart, and the aortic valve area. Each sensor independently acquires local heart sound signals, and the analog signals are synchronously transmitted to the central processing unit through low-noise transmission lines. To ensure signal synchronization, the system employs hardware-level clock synchronization technology, ensuring all sensors operate on a unified time reference and avoiding phase distortion caused by sampling time differences. For example, in the diagnosis of paroxysmal supraventricular tachycardia, synchronously acquired heart sound signals can clearly present the temporal relationship of atrioventricular contraction, aiding in the identification of abnormal conduction pathways. Signal preprocessing is a crucial step in ensuring acquisition quality. Raw heart sound signals are often accompanied by interference from breath sounds, gastrointestinal sounds, and environmental noise. Multi-channel systems require independent adaptive filtering for each channel. By dynamically adjusting filtering parameters, the system can selectively suppress noise in specific frequency bands while preserving the effective components of heart sounds. For example, in patients with mitral valve prolapse, a synchronous acquisition system can clearly separate systolic clicks from murmurs, avoiding misinterpretations caused by signal aliasing in traditional single-channel auscultation. Furthermore, the multi-channel design supports spatial filtering techniques, further eliminating non-physiological interferences such as skin friction rubs by comparing signal differences between different channels.
The core value of synchronous acquisition lies in supporting the spatiotemporal analysis of complex heart sound signals. Traditional auscultation relies on the physician's subjective judgment of a single sound source, while multi-channel systems can construct a three-dimensional acoustic distribution map of the heart. For example, in the diagnosis of hypertrophic obstructive cardiomyopathy, the system can quantify the conduction range and intensity gradient of systolic murmurs by simultaneously acquiring signals from the apex of the heart and the second intercostal space at the right sternal border, aiding in the assessment of the degree of left ventricular outflow tract obstruction. This improved spatial resolution allows doctors to more accurately locate lesions, providing an objective basis for treatment selection.
The integration of artificial intelligence algorithms further unlocks the potential of multi-channel synchronous acquisition. Deep learning models can jointly analyze synchronously acquired multi-channel heart sound signals, automatically identifying characteristic heart sound patterns. For example, in congenital heart disease screening, the system can detect specific murmurs of diseases such as atrial septal defects and ventricular septal defects by comparing the spatiotemporal distribution characteristics of normal and abnormal heart sounds. This multi-dimensional data-based analysis method significantly improves the sensitivity and specificity of diagnosis, especially suitable for situations where primary healthcare institutions lack experienced auscultators.
Multi-channel synchronous acquisition technology also enables telemedicine and remote consultations. Acquired multi-channel heart sound data can be transmitted to a cloud platform in real time. Experts can remotely guide primary care physicians in diagnosis by analyzing the synchronously acquired signals. For example, in remote areas, primary care physicians use portable multi-channel electronic stethoscopes to collect patients' heart sounds and simultaneously upload them to higher-level hospitals. Experts can make accurate judgments based on complete spatiotemporal information of the heart sounds, avoiding misdiagnosis or missed diagnosis due to signal gaps.
Clinical validation has demonstrated that multi-channel simultaneous acquisition technology significantly improves the diagnostic efficacy of complex cardiac diseases. In the diagnosis of arrhythmias, the system, by simultaneously acquiring heart sounds and electrocardiogram signals, can clearly display the temporal relationship between atrial and ventricular contractions, aiding in the identification of abnormalities such as atrioventricular dissociation and atrioventricular block. In the assessment of valvular heart disease, the multi-channel acquisition of valvular closure sounds and turbulence sounds provides quantitative indicators for grading the severity of valvular stenosis or regurgitation.
The multi-channel simultaneous acquisition technology of the medical electronic stethoscope, through the deep integration of hardware design, signal processing, artificial intelligence, and telemedicine, constructs a complete technological chain from signal acquisition to diagnostic decision-making. This technology not only improves the accuracy of cardiac disease diagnosis but also promotes the digitalization and intelligentization of auscultation techniques, providing a powerful tool for the early screening and precision treatment of cardiovascular diseases.
