Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor

This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

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Distance variable improvement of time-series big data stream evaluation

Journal Of Big Data ◽

10.1186/s40537-020-00359-w ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ari Wibisono ◽

Petrus Mursanto ◽

Jihan Adibah ◽

Wendy D. W. T. Bayu ◽

May Iffah Rizki ◽

...

Keyword(s):

Time Series ◽

Standard Deviation ◽

Standard Method ◽

Time Series Data ◽

Series Data ◽

Percentage Error ◽

Traffic Demand ◽

Incremental Model ◽

Chernoff Bound ◽

The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

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Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽

10.1177/2158244020918277 ◽

2020 ◽

Vol 10 (2) ◽

pp. 215824402091827

Author(s):

Oluwabunmi O. Adejumo

Keyword(s):

Time Series Data ◽

Optimal Growth ◽

Series Data ◽

Data Sets ◽

African Countries ◽

Limits To Growth ◽

Development Indicators ◽

Long Run ◽

Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

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A New Imputation Method for Treating Missing Precipitation Records

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1488 ◽

2014 ◽

Vol 635-637 ◽

pp. 1488-1495

Author(s):

Yu Liu ◽

Feng Rui Chen

Keyword(s):

Time Series Data ◽

Absolute Error ◽

Imputation Method ◽

Temporal Information ◽

Series Data ◽

Precipitation Trend ◽

Monthly Total Precipitation ◽

The Mean ◽

Spatio Temporal ◽

Precipitation Records

This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.

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Accounting Implications of Micro-Fiscal Measures and Quality of Real Gross National Goods and Services: Empirical Evidence From Nigeria

Research in World Economy ◽

10.5430/rwe.v11n6p155 ◽

2020 ◽

Vol 11 (6) ◽

pp. 155

Author(s):

Akabom I. Asuquo ◽

Arzizeh Tiesieh Tapang ◽

Uwem E. Uwah ◽

Nicholas O. Dan ◽

Ashishie Peter Uklala

Keyword(s):

Empirical Evidence ◽

Inverse Relationship ◽

Time Series Data ◽

Series Data ◽

Government Agencies ◽

Exploratory Research ◽

Direct Effects ◽

Goods And Services ◽

The Government

The study explored into accounting implications of micro-fiscal measures and quality of real gross national goods and services: empirical evidence from Nigeria for a period of thirty years. The objective was to examine how micro-fiscal measures affect real gross national goods and services using thirty years’ time-series data. The exploratory research methodology was applied and data collected were analysed using multiple regression and other statistical techniques. Findings of the study revealed that significant and direct effects were exerted on gross national goods and services by all the known and identified micro-fiscal measures in the review, except swap and levy ratios which had inverse relationship as revealed by their coefficients obtained from the analysis. Therefore, the government and government agencies have a duty to control macro-fiscal activities in terms of creation of national goods, wealth and services using the identified micro-fiscal mechanisms as the basis for decisions and policies making besides implementation.

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Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

International Journal of Humanoid Robotics ◽

10.1142/s0219843619500166 ◽

2019 ◽

Vol 16 (04) ◽

pp. 1950016 ◽

Cited By ~ 1

Author(s):

Duanpo Wu ◽

Zimeng Wang ◽

Hong Huang ◽

Guangsheng Wang ◽

Junbiao Liu ◽

...

Keyword(s):

Detection System ◽

Epileptic Seizures ◽

Seizure Detection ◽

Spike Rate ◽

Linear Discriminant ◽

Positive Ratio ◽

False Positive Ratio ◽

Eeg Data ◽

The Family

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

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Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

Frontiers in Energy Research ◽

10.3389/fenrg.2021.639346 ◽

2021 ◽

Vol 9 ◽

Author(s):

Moritz Stüber ◽

Felix Scherhag ◽

Matthieu Deru ◽

Alassane Ndiaye ◽

Muhammad Moiz Sakha ◽

...

Keyword(s):

Smart Grids ◽

Time Series Data ◽

Short Term Memory ◽

Data Driven ◽

Series Data ◽

Small Scale ◽

Pv System ◽

Advantages And Disadvantages ◽

Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

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Inferring phage-bacteria infection networks from time series data

10.1101/051581 ◽

2016 ◽

Author(s):

Luis F. Jover ◽

Justin Romberg ◽

Joshua S. Weitz

Keyword(s):

Time Series ◽

Time Course ◽

Community Dynamics ◽

Time Series Data ◽

Plaque Assay ◽

Interaction Network ◽

Series Data ◽

Factors Affecting ◽

Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

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Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v2i2.2813 ◽

2011 ◽

Vol 2 (2) ◽

pp. 636 ◽

Cited By ~ 2

Author(s):

Iwa Sungkawa ◽

Ries Tri Megasari

Keyword(s):

Time Series Data ◽

Moving Average ◽

Forecast Accuracy ◽

Exponential Smoothing ◽

Series Data ◽

Percentage Error ◽

Forecasting Model ◽

Data Series ◽

Past Data ◽

The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape.

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Growth and Instability Analysis of Groundnut Production in India and Karnataka

Economic Affairs ◽

10.46852/0424-2513.1.2021.8 ◽

2021 ◽

Vol 66 (1) ◽

Author(s):

Akshata Nayak

Keyword(s):

Time Series Data ◽

Cultural Practice ◽

Series Data ◽

Policy Makers ◽

Productivity Level ◽

Predominant Component ◽

Average Production ◽

Change In The Mean ◽

The Mean ◽

The Tropics

Groundnut is grown throughout the tropics and extended to the subtropical countries. India is the second largest producer of groundnut in the world after China. The fact that groundnut crops in India, particularly in Karnataka are mainly covered under rain fed situation, which in turn has to depend on the arrival of monsoon, climatic changes and drought. Hence, the productivity level of groundnut crop was erratic. It was in this backdrop, an attempt was made through the present study to examine the growth and instability of groundnut in India and Karnataka by way of analyzing the time series data of 48 years. The results revealed that the level of instability was marginally higher in groundnut area (8.7 %) during period II compared to period I (2.9%) and period III (7.3%). The variation in production and yield of groundnut was higher during the period III compared to period I and II. Change in the mean area is contributing more to change in average production of groundnut in India and in Karnataka followed by interaction between changes in mean area and mean yield. Change in area variance is the predominant component contributing to the change in variance of production of groundnut in India as well as in Karnataka. From the outcome of the result, it is concluded that the researchers and policy makers have to take more attention to develop location specific cultural practice to increase and sustain groundnut production and yield in the nation.

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Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

Jurnal Aktual Akuntansi Keuangan Bisnis Terapan (AKUNBISNIS) ◽

10.32497/akunbisnis.v1i1.1235 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Moh.Hasanudin Marliyati ◽

Sri Murtini ◽

Resi Yudhaningsih ◽

Retno Retno

Keyword(s):

Information Technology ◽

Time Series ◽

Communication Skills ◽

Time Series Data ◽

Soft Skills ◽

Series Data ◽

Central Java ◽

Hard Skills ◽

Main Components

This research aimed at exploring the quality of accounting diploma students during their internship program in industries. The term of student’s quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and analyzed using statistic descriptive. The result showed that average students’ quality during their internship was good. Among the 5 skills observed, the corresponding industries ranked teamwork skills as the highest, followed by independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.

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