Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.

Color spectrographic respiratory monitoring from the external ear canal.

The need for simple and reliable means of respiratory monitoring has existed since the beginnings of medicine. In the present study, we describe the use of color spectrographic analysis of breathing sounds recorded from the external ear canal as a candidate technology to meet this need. A miniature electret microphone was modified with the addition of an adapter to allow it to be placed comfortably in the external ear canal. The amplified signal was then connected to a real-time color spectrogram program running on a laptop personal computer utilizing the Windows operating system. Based on the results obtained, we hypothesize that the real-time display of color spectrogram breathing patterns locally or at a central monitoring station may turn out to be a useful means of respiratory monitoring in patients at increased risk of respiratory depression or other respiratory problems. Finally, we conducted a statistical analysis that suggests that significant spectrogram differences may exist among some groups investigated in the study.

Analysis of central venous pressure (CVP) signals using mathematical methods.

Central venous pressure (CVP) is an important clinical parameter for physicians but only the absolute CVP value is typically monitored in the intensive care unit (ICU). In this study, we propose a novel mathematical method to present and analyze CVP signals. A total of 44 suitable samples were chosen from a total of 65 collected in an ICU. Pre-processing of the samples included rate reduction and digital filtering. The statistical features of time and frequency domain, wavelet, and empirical mode decomposition of these signals were extracted. We found no significant difference among the CVP signals regarding sex, smoking, coronary disease, and respiration mode of the samples.

A multi-channel acoustics monitor for perioperative respiratory monitoring: preliminary data.

This study pertains to a six-channel acoustic monitoring system for use in patient monitoring during or after surgery. The base hardware consists of a USB data acquisition system, a custom-built six-channel amplification system, and a series of microphones of various designs. The software is based on the MATLAB platform with data acquisition drivers installed. The displayed information includes: time domain signals, frequency domain signals, and tools to aid in the detection of endobronchial intubation. We hypothesize that the above mentioned arrangement may be helpful to the anesthesiologist in recognizing clinical conditions like wheezing, bronchospasm, endobronchial intubation, and apnea. The study also evaluated various types of microphone designs used to transduce breath sounds. The system also features selectable band-pass filtering using MATLAB algorithms as well as a collection of recordings obtained with the system to establish what respiratory acoustic signals look like under various conditions.