We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed. After that, the application determines whether the patient suffers from AFib or not. The presented method has high potential to detect paroxysmal (“silent”) AFib from large masses. In this paper, we present the preprocessing, feature extraction, feature analysis, and classification results of the envisioned AFib detection system based on clinical data acquired with a standard mobile phone equipped with Google Android OS. Test data was gathered from 16 AFib patients (validated against ECG), as well as a control group of 23 healthy individuals with no diagnosed heart diseases. We obtained an accuracy of 97.4% in AFib versus healthy classification (a sensitivity of 93.8% and a specificity of 100%). Due to the wide availability of smart devices/sensors with embedded IMU, the proposed methods could potentially also scale to other domains such as embedded body-sensor networks.

We describe a home health monitoring solution with cardiac beat-to-beat detection using accelerometer and gyroscope signal fusion. The proposed method measures both the precordial translational and rotational motions of the chest using miniaturized inertial sensors. The algorithm removes motion artefacts, selects the best axis from multi-axial accelerometric and gyroscopic signals and detects the location of beats using two detection principles based on i) the signal envelope and ii) signal morphology. We consider the beat-to-beat detection accuracy, estimate the heart rate and compare the detection performance between the sensor modalities in two study groups: i) healthy subjects and ii) heart disease patients. The average true and positive prediction rates of beat detection were 99.9% and 99.6% for the healthy subjects and up to 95.9% and 95.3% for the heart disease patients, respectively. Although high-accuracy beat detection was achieved for the heart disease patients, location matching in these patients was found to be less accurate compared to that of the healthy subjects. The average root mean square error (RMSE) between the mechanical and ECG interbeat intervals was 5.6 ms for the healthy patients; this error increased approximately ten-fold for the heart disease patients. Similarly, the RMSE for the averaged heart rate estimation showed about a ten-fold difference at 1.05 beats per minute (bpm) for the heart disease patients. The used sensor modalities are found in many electronic devices, such as smartphones and wearable technologies and the method provides a step towards ubiquitous cardiac monitoring.

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this study, we present a comprehensive time-frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG-GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm (SR) cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib=150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib=40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained respectively for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.

Timely diagnosis of cardiovascular diseases (CVD) is crucial to prevent morbidity and mortality. Atrial fibrillation (AFib) and heart failure (HF) are two prevalent cardiac disorders that are associated with a high risk of morbidity and mortality, especially if they are concurrently present. Current approaches fail to screen many at-risk individuals who would benefit from preventive treatment; while others receive unnecessary interventions. An effective approach to the detection of CVDs is mechanocardiography (MCG) by which translational and rotational precordial chest movements are monitored. In this study, we collected MCG data from a study sample of 300 hospitalized cardiac patients using multidimensional built-in inertial sensors of a smartphone. Our main objective was to detect concurrent AFib and acute decompensated HF (ADHF) using smartphone MCG (or sMCG). To this end, we adopted a supervised machine learning classification using multi-label and hierarchical classification. Logistic regression, random forest, and extreme gradient boosting were used as candidate classifiers. The results of the analysis showed the area under the receiver operating characteristic curve values of 0.98 and 0.85 for AFib and ADHF, respectively. The highest percentages of positive and negative predictive values for AFib were 91.9 and 100; while for ADHF, they were 56.9 and 88.4 for the multi-label classification and 69.9 and 68.8 for the hierarchical classification, respectively. We conclude that using a single sMCG measurement, AFib can be detected accurately whereas ADHF can be detected with moderate certainty.

The research reported in this paper focuses on the modeling of Local Binary Patterns (LBPs) and presents an a priori model where LBPs are considered as combinations of permutations. The aim is to increase the understanding of the mechanisms related to the formation of uniform LBPs. Uniform patterns are known to exhibit high discriminative capability; however, so far the reasons for this have not been fully explored. We report an observation that although the overall a priori probability of uniform LBPs is high, it is mostly due to the high probability of only certain classes of patterns, while the a priori probability of other patterns is very low. In order to examine this behavior, the relationship between the runs up and down test for randomness of permutations and the uniform LBPs was studied. Quantitative experiments were then carried out to show that the relative effect of uniform patterns to the LBP histogram is strengthened with deterministic data, in comparison with the i.i.d. model. This was verified by using an a priori model as well as through experiments with natural image data. It was further illustrated that specific uniform LBP codes can also provide responses to salient shapes, that is, to monotonically changing intensity functions and edges within the image microstructure.

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2–90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.