Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This framework aims to improve the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.
Both remote and in-hospital healthcare mechanisms use body sensors and patient monitoring devices to collect and display the continuous signals of multiple physiological parameters, which are usually presented in the form of data streams. These data streams will be referenced by doctors or intelligent medical decision systems to detect or predict diseases, and to give accurate prognosis decisions in time [
Some researches [
Fog computing is the current optimal solution for ehealth services [
In this paper, we propose an environment-friendly Medical data stream management system based on fog computing, called FogMed. It can collect and store patients’ physical information, analyze and predict anomalies based on the information, and optimize QoSs of fog services. The purpose of monitoring patients is to detect or predict their anomalies as early as possible, so that the accurate treatments can be taken in advance to maintain patient health. Therefore, we focus on two problems: anomaly detection and prediction of Medical data streams, and QoS optimization of fog services. To demonstrate the efficiency of FogMed, we illustrate an example of Atrial Fibrillation (AF) prediction from Electrocardiogram (ECG) data streams. A two-layer stacked long short term memory (LSTM) model is proposed for AF prediction (abbreviated as SLAP). Based on the AF prediction results, we evaluate the response time of fog nodes for processing simple Medical tasks and that of CCC for processing complex Medical tasks. At last, we build a virtual FogMed environment and conduct comprehensive experiments. Experiment results show that FogMed performs better than the cloud computing model for processing both complex and simple Medical tasks. Overall, this work has the following contributions: We introduce a fog-based disease prediction framework to explore the correlation between QoS optimization of fog computing and the performance of disease prediction. We illustrate an example of heartbeat anomaly prediction based on FogMed and introduce a deep learning model for anomaly prediction. We conduct comprehensive experiments to validate the efficiency of FogMed for processing both complex and simple Medical tasks.
The structure of this paper is: Section 2 describes the architecture, functions and characteristics of FogMed, and illustrates an example of abnormal heartbeat prediction based on FogMed; Section 3 demonstrates the efficiency of FogMed based on experiments. Section 4 reviews state-of-the-art; and Section 5 concludes this paper.
This section introduces the key physiological parameters that need to be monitored and analyzed in ehealth. We propose a system of analyzing, mining and managing Medical sensor data streams based on fog computing techniques, namely FogMed, and introduce the structure of a fog device in FogMed. We summarize the main notations used in this paper in
Notation | Meaning |
---|---|
a fog node | |
the |
|
a controlling fog device | |
CCC | cloud computing center |
AF | Atrial Fibrillation |
SLAP | stacked LSTM for AF detection |
input of the bottom hidden layer of SLAP | |
weight of an edge connecting upper-layer and bottom-layer cells | |
output of a cell of SLAP | |
the |
|
true value of |
|
probability distribution of error |
|
an error threshold for distinguishing normal and abnormal ECG samples |
Advanced handheld or wearable monitors can help collect different types of physiological signals of patients or elders. For example, a handhold multi-parameter patient monitor [
Real-time generated healthcare data streams have features of variety, large volume, and high velocity [
The architecture of FogMed is shown in
We use the round-robin database (RRDB) in fog layer to store small and temporal data streams; and use both the RRDB and the journal database (JDB) in cloud layer to store large and long-term historical data [
FogMed works in a constrained geographic area. It further partitions devices of a wide area into a number of subareas and manages each subarea through some controlling devices.
Resource management: Manages both practical and virtual resources to guarantee successful executions of multiple Medical tasks, by simultaneously optimizing QoS levels [ Power management: Power management is critical in an ehealth system. Both cloud and fog devices have power constraints [ Monitoring devices: Monitor both resource utilization and energy consumption. The monitoring data can be used for real-time resource and energy management. It can be stored in a cloud JDB for subsequent system analyses. Medical data stream management: Pre-process, store, and analyze Medical data streams. Especially, by cooperating with the resource management, energy management and status monitoring components, a fog node should be capable of determining which data streams and tasks should be sent to cloud and which should be processed by fog devices.
Basically, non-controlling fog devices in a subarea have functions of Medical data stream management, status monitoring and power management. They execute instructions from controlling devices and send information to places designated by controlling devices (e.g., databases, other fog devices, user terminals or cloud devices).
The structure of a non-controlling fog device is similar to that of a controlling fog device, but based on its specific functions, it might be configured by different software or hardware.
The application scenario of FogMed is to monitor physiological conditions of patients by using sensors and monitors inside or outside a hospital. The workflow of Fogmed is as follows: By using monitor components (see A fog device is deployed by a system of Medical data stream management, which includes the services of real-time processing and analyzing consistently generated data streams. Based on specific requirements, the fog device calls corresponding services to analyze data streams. If this analyzing task requires a short-term history information, the fog device accesses the RRDB. However, if a long-term history information is required and the fog device cannot provide such information, this analyzing task is sent to CCC. In this step, we need to develop innovative techniques to predict or detect anomalies in data streams to diagnose diseases as early as possible. As soon as an anomaly is detected, the system calls services of analyzing anomalies and making treatment decisions. It then sends alarms or corrective treatments to end-devices of the patient based on some pre-determined confidence thresholds. If the system cannot automatically make right decisions based on the confidence thresholds, the anomaly will be sent to end-devices, and then artificial decisions will be made and sent to patients. In this step, we need to develop techniques of anomaly prediction and treatment decision making.
The other two objectives of this work are QoS optimization and energy saving. FogMed guarantees that the requested tasks are completed, and the best trade-off among multiple quality measuring parameters (e.g., response time, latency, and availability) of services (e.g., cloud, fog and network resources) is achieved. The satisfaction degrees of a service user (e.g., a patient, a doctor, or a service using the measured services) are then optimized. Simultaneously, FogMed aims to minimize the energy (e.g., power) consumption on the premise of QoS optimization and accurate decision making.
To achieve QoS optimization, we explore techniques of resource management, including both software and hardware resources. Software resources refer to virtual resources, e.g., virtual machines on a fog or cloud platform, virtual fog nodes by separating an actual fog node or a geographically constrained area, and virtual local area networks. Hardware resources include the actual fog and cloud processors, memories, and networks. FogMed must be able to dynamically determine how many and which fog and cloud processors to be used, how many fog and cloud databases to be built, and how to distribute tasks and balance loads on fog and cloud devices. We will show the efficiency of FogMed for performing Medical tasks by comparing it with a CCC in Section IV.
A cloud computing device in cloud layer processes complex services and a large amount of data that cannot be processed in fog devices. In our medical context, the data stream mining services that do not require fast responses but require high computing power or depend on certain historical information are most probably processed by cloud computing devices. In addition, cloud layer is deployed with both JDB and RRDB, which store long-term information and time-critical information respectively. Components of cloud layer are shown in
In cloud layer, we store a large amount of history ECG data streams in JDBs, and train a deep learning model, a stacked LSTM, to predict AFs based on ECG data streams. We call this model a stacked long short term memory architecture for AF prediction (abbreviated as SLAP). The structure of SLAP is shown in
SLAP takes advantage of the multiplicative structure of LSTM, in which each cell is capable of controlling the information flow. Based on the output of a time unit and the new ECG signals, SLAP predicts signal trends in next time unit with variable time lengths.
After training a SLAP model, we determine whether the predicted samples are normal or abnormal. To achieve this, we assume that the errors of the predicted samples from the true samples fit the multivariate Gaussian distribution. Suppose we have
where
If
Steps of building SLAP are as follows: (1) Data processing. At first, we divide a set of multivariate ECG samples into four subsets,
where
Our experiment contains two main parts: Experiment-I: evaluate the deep learning model SLAP for AF prediction; and Experiment-II: Evaluate the response time of fog layer and cloud layer for processing AFs. We describe the results of these two parts separately.
Experiment-I is based on two public ECG databases: the long-term AF database (LTAF) and the AF terminal challenge database (AFTC) [
The settings for training an SLAP are defined as follows: divide a long term ECG into short segments (1~2 minutes), where each segment has the length of 100 unit time (1 unit time = 1 second); and input the segments into the input layer of SLAP consistently. SLAP has two hidden layers. Each layer has 55 cells. Let the learning rate to be 0.01, the termination error to be 0.00001, and
Based on the above settings, we train an SLAP model and use this model to test the performance of AF prediction and then to determine
Model | RNN | LSTM | SLAP |
---|---|---|---|
Accuracy | 0.83 | 0.87 | 0.92 |
Recall | 0.87 | 0.88 | 0.92 |
F-score | 0.83 | 0.87 | 0.92 |
Layer number | 1 | 2 | 3 |
---|---|---|---|
Accuracy | 0.87 | 0.92 | 0.84 |
Recall | 0.88 | 0.92 | 0.7 |
F-score | 0.87 | 0.92 | 0.84 |
We build a virtual FogMed environment, whose topology is shown in
Configuration | Value | Configuration | Value |
---|---|---|---|
cdcCoreNum | 64 | aveTaskSizeF2C | 1 (KB) |
fogCoreNum | 4 | aveQuerySizeF2DB | 1 (KB) |
aveTransRateF2C | 1 (MB/s) | aveCloudRespSize | 10 (KB) |
aveTransRateF2DB | 10 (MB/s) | aveDBRespSize | 50 (KB) |
aveTransRateF2E | 5 (MB/s) | aveDBQueryTime | 1 (ms) |
In the virtual FogMed, patients are monitored to detect the abnormal heartbeats. By using ECG detection sensors, a patient generates heartbeat data streams consistently. These data streams are sent to and analyzed by a mist device connected with the sensors. If an abnormal heartbeat happens, a warning signal is sent to a connected fog node
We test AFs in this experiment, and define the number (
Test-I: a fog node sends all arrived tasks (including
Test-II: a simple task (
From
From this experiment, we can see that distributing all tasks to cloud has to solve problems of remote transmission, network congestion and task overwhelming. However, FogMed takes advantage of both fog and cloud resources, which solves the problem that the fog layer lacks computation and storage resources, and eases network congestion and task overwhelming for the cloud layer. In addition, for small hospitals or individuals, it is simpler and more practical to build or use cheap fog devices near to them. Overall speaking, fog computing is more appropriate to solve ehealth problems and FogMed is an efficient and practical framework for disease diagnosis and prognosis.
This section reviews state-of-the-art about the fog-based ehealth, focusing on discussing disease prognosis, Medical data stream mining, QoS optimization and energy saving. Based on the literature review, we point out some unresolved issues.
Many scholars explored the problem of QoS optimization in fog environment. Muhammed et al. [
Sensor-based fog computing faces an important problem: How to balance the limited power of fog devices and the requirements on processing a large amount of data. To solve this problem, Sodhro et al. [
Farahani et al. [
Some researchers explored the disease prognosis techniques without considering the network environment and the computing paradigm. For example, Yoon et al. [
The main objective of FogMed is to support disease prediction based on Medical data streams. The above works are not appropriate for solving the problem considered in FogMed. To achieve the objective, FogMed should have a mechanism to automatically segment a long-term Medical data stream precisely, learn association rules between a preceding segment and a future abnormal segment, and allocate and store the segments and rules properly to support fast information query. Therefore, QoS optimization strategies for making predictive Medical decisions should consider how to transmit, allocate and store the correlated segments and rules in a fog environment, and when to perform predictions and make decisions based on the rules and the current QoS levels of fog services. In addition, few of the existing works investigate how fog computing techniques will affect the design of Medical data stream mining algorithms, and then affect the disease prediction performance. Therefore, it is necessary to design adaptive Medical data stream mining algorithms to fit the dynamic environment variables of fog computing.
In this work, we implemented a deep learning algorithm for anomaly prediction and validated the capability of Fog computing processing anomalies in Medical data streams in terms of the average response time. In the future, we will consider more QoS variables and explore how these QoS variables influence disease prediction tasks.
We proposed FogMed, a fog-based framework for disease prognosis based on Medical sensor data streams. FogMed aims to improve the prognosis accuracy by achieving two objectives: QoS optimization of fog services, and anomaly detection and prediction based on Medical data streams. In addition, we built a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset. In the future, we will consider power constraints of fog devices and develop advanced power saving techniques in FogMed. Furthermore, we will explore power-aware task allocation algorithms to improve the anomaly prediction performance in power-constrained environment.