Challenges in investigation of diabetes-related aviation fatalities—an analysis of 1491 subsequent aviation fatalities in USA during 2011–2016

Diabetes mellitus (DM) could cause pilot incapacitation and result in aviation fatalities. The mechanisms could be directly as a consequence of acute hypoglycemia/subacute diabetic ketoacidosis (DKA) or indirectly as an acute cardiovascular event by contributing to the development of atherosclerosis in coronary or carotid and cerebral arteries. In this study, DM-related fatal flight accidents in the US National Transport Bureau’s database between years 2011–2016 were analyzed with special emphasis on postmortem (PM) glucose levels and correlation of toxicological reports with anamnestic information on DM. Additionally, autopsy results on coronary arteries were reviewed. In 43 out of 1491 (~ 3%) fatal accidents pilots had DM. Postmortem glucose or glycated hemoglobin percentage (Hb1Ac) was measured in 12 of the 43 cases; while antidiabetic medication was found in 14 of the cases (only two of the cases had both glucose measurements and medication). With the increasing prevalence of DM, a possibility of pilot incapacitation due to DM or complications of DM should be actively studied, even if no anamnestic information of DM was available. While PM hypoglycemia is difficult to assess, we propose a systematic investigation based on measurement of glucose, Hb1Ac%, and ketone bodies, and documentation of atherosclerotic lesions in major arteries to identify or rule out DM as a cause of pilot incapacitation.

     

Fuzzy detection of EEG alpha without amplitude thresholding

Intelligent automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need detection of different electroencephalogram (EEG) waveforms. The problem in automated detection of alpha activity is the large inter-individual variability of its amplitude and duration. In this work, a fuzzy reasoning based method for the detection of alpha activity was designed and tested using a total of 32 recordings from seven different subjects. Intelligence of the method was distributed to features extracted and the way they were combined. The ranges of the fuzzy rules were determined based on feature statistics. The advantage of the detector is that no alpha amplitude threshold needs to be selected. The performance of the alpha detector was assessed with receiver operating characteristic (ROC) curves. When the true positive rate was 94.2%, the false positive rate was 9.2%, which indicates good performance in sleep EEG analysis.     

The diagnostic value of video capsule endoscopy

Background and aimsVideo capsule endoscopy (VCE) offers the best means of studying small bowel, but is expensive. We investigated how physicians appraise the value of VCE.MethodsA questionnaire was sent to remitting physicians comprising questions on the value of VCE in altogether 189 adults. The follow-up time was at least one year. The patient history was also scrutinized in case records.ResultsThe most common indications for VCE were anaemia (n = 100) or gastrointestinal bleeding (n = 21) (60%), suspicion of Crohn's disease (21%), abdominal pain (9%) and coeliac disease (6%). The diagnostic value of VCE was rated best in patients suffering from anaemia or bleeding, being decisive or beneficial in 33% (OR 2.3, CI = 1.1–4.8 compared to all series) and helpful in exclusion in an additional 36%. In Crohn's disease, VCE was helpful in the exclusion of intestinal lesions in 50% of cases; in coeliac disease the corresponding percentage was 42%. When abdominal pain was the only indication for VCE, the examination was beneficial in one patient only.ConclusionsThe diagnostic value of VCE was evident in patients with anaemia or gastrointestinal bleeding. The procedure was deemed to be helpful in exclusion also in Crohn's disease and in coeliac disease. Abdominal pain was a rare indication, and the diagnostic yield limited.     

The use of two-channel electro-oculography in automatic detection of unintentional sleep onset

An automatic method was developed for detecting unintentional sleep onset. The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. An automatic estimation of slow eye movements (SEM) was developed and used as the main criterion to separate sleep stage 1 (S1) from wakefulness. Additionally synchronous electroencephalographic (EEG) activity of sleep stages 1 and 2 was detected by calculating cross-correlation and amplitude difference in the 1.5-6 Hz theta band between the two EOG channels. Alpha power 8-12 Hz and beta power 18-30 Hz were used to determine wakefulness. Unintentional sleep onsets were studied using data from four separate maintenance of wakefulness test (MWT) sessions of 228 subjects. The automatic scoring of 30s sleep onset epochs using only EOG was compared to standard visual sleep stage scoring. The optimal detection thresholds were derived using data from 114 subjects and then applied to the data from different 114 subjects. Cohen's Kappa between the visual and the new automatic scoring system in separating wakefulness and sleep was substantial (0.67) with epoch by epoch agreement of 98%. The sleep epoch detection sensitivity was 70% and specificity 99%. The results are provided with a 1s delay for each 30s epoch. The developed method has to be tested in field applications. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive self-applicable electrodes.     

An Intelligent Ballistocardiographic Chair using a Novel SF-ART Neural Network and Biorthogonal Wavelets

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.     

Major depression and fitness to fly by different aviation authorities

Safety issues are paramount in aviation and careful treatment protocols have been developed to ensure fitness to fly among aviators recovering from major depressive episodes (MDE). Aeromedical examiners (AMEs) do not necessarily treat depressive patients frequently, so they often consult psychiatrists; however, psychiatrists are rarely familiar with aviator treatment protocols. U.S., Canadian, and Australian regulations allow several choices among antidepressant drugs for flying pilots recovering from an MDE. Symptom stability times before the possible return to flying duties vary from 4 wk to 12 mo. So far European regulations have not allowed antidepressants, but the situation may change.     

Low metacarpal index predicts hip fracture: a prospective population study of 3,561 subjects with 15 years of follow-up

Background Metacarpal index (MCI), measured from hand radiographs as the ratio between combined cortical thickness and bone diameter, has been suggested for assessment of bone mass and risk of osteoporotic fracture. We studied MCI for its ability to predict hip fractures.

Methods Hand radiographs were taken and MCI determined in 3,561 subjects from a representative population sample of 8,000 Finns who were 30 years of age or over in 1978-80. Record linkage to the National Hospital Discharge Register identified 117 subjects who had been hospitalized for primary treatment of hip fracture by the end of 1994.

Results High age, low body mass index, tall stature and smoking at baseline showed, independently of each other, significant associations with low MCI. Low MCI was a strong predictor of hip fracture. When adjusted for all potential confounding factors, the relative risk of hip fracture per decrement of MCI by one standard deviation (0.1) was 1.5 (95% CI 1.2-1.8).

Interpretation Low MCI is associated with known risk factors of osteoporosis and predicts hip fracture. Since hand radiographs are easily available at low cost, measurements of MCI can be used as an alternative approach to find osteoporotic individuals with a high risk of hip fracture. ▪     

Automatic quantification of light sleep shows differences between apnea patients and healthy subjects.

A fully automatic method to quantify sleep depth during the night was developed in the present work. The method was tested using 20 all-night recordings from 10 healthy control subjects and 10 sleep apnea patients. The results showed statistically significant differences in sleep depth between control subjects and sleep apnea patients. The overall sleep was lighter in apnea patients than in healthy control subjects, most likely indicating a disturbed sleep caused by apneas. The automatic parameters presented provide a method to quantify the light sleep and could in the future possibly be used in clinical sleep studies and follow-up of treatment.