Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.

Experimental Study of Assessment on Ventricular Activation Origin and Contraction Sequence by Doppler Tissue Imaging

To evaluate the possibility and accuracy of Doppler tissue image (DTI) on assessment of normal and abnormal ventricular activation and contraction sequence, 9 open chest canine hearts were analyzed by acceleration mode, M mode, and spectrum mode DTI. Our results showed that: (1) Acceleration mode DTI could show the origin of activation and conduction sequence on line; (2) M mode DTI revealed that the activation in mid interventricular septum was earlier than that in mid left ventricular posterior wall at sinus activation; (3) Spectrum DTI showed the ventricular endocardium was activated earlier than the ventricular epicardium in all segments at sinus rhythm. The earliest site of activation of the normal ventricular wall was at middle interventricular septum; the latest site was at basal posterior wall; the contraction sequence was different at the different walls; (4) During abnormal ventricular activation, mid left ventricular posterior wall was activated earliest in accordance with the pacing sites. Abnormal ventricular activation was slower than sinus activation, and the contraction sequence varied at different sites of ventricular wall. It is concluded that DTI can be used to localize the origin of normal or abnormal myocardial activation and to assess the contraction sequence conveniently, accurately and non invasively.

Mechanism of gastrointestinal abnormal motor activity induced by cisplatin in conscious dogs

AIM: To investigate whether 5-hydroxytryptamine (serotonin; 5-HT) is involved in mediating abnormal motor activity in dogs after cisplatin administration.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.