Résumé: Physical examination of the thorax is key to the clinical diagnosis of respiratory diseases. Among other examination techniques, palpation evaluates the transmission of high-frequency vibrations produced by vocalizations (tactile fremitus), which helps the physicians to identify abnormalities within the respiratory system. We propose the use of an airborne ultrasound surface motion camera (AUSMC) to quantitatively map the vibrations induced by subject vocalization. This approach could make the examination of vocal fremitus quantifiable, reproducible, and archivable. Massive data collection of vocal fremitus could allow using artificial intelligence algorithms to isolate vibration patterns that could help disease identification. Until now, in contrast, the interpretation of vocal fremitus has been subject to the physician’s experience and remains subjective. In the present work, we demonstrate the capabilities of the AUSMC to measure vocal fremitus thoracic vibration maps on 77 healthy volunteers. We have observed a spatial dependence of vibration maps on vocalization frequency. We observed that the left lung generates fewer surface vibrations than the right one, which was expected according to their respective dimensions. We also discuss the implications of our findings.

Résumé: Emotion is a complicated state of mind, which normally reflects human perceptions and attitudes. Proper recognition of emotional states and its quality plays crucial role for the detection of critical diseases and subsequent treatment procedures. Generally, multi-lead, complicated Electroencephalogram (EEG) based analysis predominate the characterization of emotion detection. Nowadays, user-friendly, rich-cardiac-information and wearable characteristics of the photoplethysmogram (PPG) signal are also being used to identify the emotional states. However, a majority of the reported emotion detection techniques mostly uses PPG signal in multimodality approach. In this paper, a simple methodology is proposed to identify multiple emotional states via the analysis of the PPG signal alone. Normally, emotion induced alteration in the heart rate causes variation in the blood ejection rate and a subsequent deviation in the balance of the systolic and the diastolic phases. Consequently, a specific time-domain characteristic is identified to quantify such imbalance and its variability is then used as a feature to discriminate between the five most prominent emotional states via a threshold-based classification technique. The algorithm presents superior performance while evaluated on the PPG data collected from the standard DEAP dataset with an average detection accuracy of 97.78%. Compared to existing literatures, the superior results establish the effectiveness of the proposed algorithm for the detection of multiple emotional states using PPG signal only. Moreover, the use of a single PPG feature and the application of a simple threshold-based classification technique also justify its promises for implementation in real-life, healthcare applications.

Résumé: Structured illumination enables the tailoring of an imaging device’s optical transfer function to enhance resolution. We propose the incorporation of a temporal periodic modulation, specifically a rotating mask, to encode multiple transfer functions in the temporal domain. This approach is demonstrated using a confocal microscope configuration. At each scanning position, a temporal periodic signal is recorded. By filtering around each harmonic of the rotation frequency, multiple images of the same object can be constructed. The image carried by the nth harmonic is a convolution of the object with a phase vortex of topological charge n, similar to the outcome when using a vortex phase plate as an illumination. This enables the collection of chosen high spatial frequencies from the sample, thereby enhancing the spatial resolution of the confocal microscope.