Measurement of the electroencephalogram (EEG) coherence, atmospheric noise, and Schumann resonances in group meditation

Electrical activity in the human body was investigated using EEG and ECG measurements while subjects remained with eyes open, eyes closed and in a meditation state. During these measurements, additional antennas were attached to the equipment to record atmospheric noise and signal activity simultaneously. The obtained data was analyzed and various observations were made. Processed data based on antenna signals clearly showed the presence of man-made signals, having narrow spectral widths that could be treated as atmospheric noise in the frequency range up to 50 Hz. In addition, signals clustered around 7.8, 14.1, 20.3, 26.4, and 32.5 Hz were observed as Schumann resonances of the earth-ionosphere waveguide. Careful analysis of the noise in the EEG and ECG signals showed the noise activity to be identical to the signals detected by the antennas. Hence, it was possible to differentiate the physiological brain and heart activity from the noise, which is now clearly identified as man-made signals and Schumann resonances up to 50 Hz. The presence of coherence in dual EEG channels is a good measure to quantify the meditation state. The performed measurements showed high coherence around 10 Hz in single subjects while meditating. When these measurements extended to two subjects, with the goal to study group meditation, it was observed that coherence spectra spread significantly to other frequencies

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

Di\v{s}imo: Anchoring Our Breath

We present a system that raises awareness about users' inner state. Di\v{s}imo is a multimodal ambient display that provides feedback about one's stress level, which is assessed through heart rate monitoring. Upon detecting a low heart rate variability for a prolonged period of time, Di\v{s}imo plays an audio track, setting the pace of a regular and deep breathing. Users can then choose to take a moment to focus on their breath. By doing so, they will activate the Di\v{s}imo devices belonging to their close ones, who can then join for a shared relaxation session

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Medical applications of diamond magnetometry: commercial viability

The sensing of magnetic fields has important applications in medicine, particularly to the sensing of signals in the heart and brain. The fields associated with biomagnetism are exceptionally weak, being many orders of magnitude smaller than the Earth's magnetic field. To measure them requires that we use the most sensitive detection techniques, however, to be commercially viable this must be done at an affordable cost. The current state of the art uses costly SQUID magnetometers, although they will likely be superseded by less costly, but otherwise limited, alkali vapour magnetometers. Here, we discuss the application of diamond magnetometers to medical applications. Diamond magnetometers are robust, solid state devices that work in a broad range of environments, with the potential for sensitivity comparable to the leading technologies.Comment: 10 pages, 1 figur

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal